Mobile device assisted smart building control

ABSTRACT

A system for signaling electronic devices based on the occupancy of one or more regions of a building includes a central controller with a wireless receiver to receive data indicative of the occupancy of a region of a building from fixed wireless sensors associated with the building. The central controller also receives estimated locations for one or more mobile wireless devices. The central controller can also receive an indication of the proximity of one or more mobile wireless device to a person. The central controller can determine one or more location estimates for at least one mobile wireless device. The central controller can weight some or all of the fixed wireless sensor occupancy indications and device location data based on the proximity indication and selects one or more output devices (e.g. wireless speakers or WiFi enables lights) to operate. The central controller can be located within a mobile wireless device. In this case the challenge for the controller is determining how to weight the relative importance of mobile device location and sensor data indicating person location. In several embodiments the disclosed system provides means for weighting the mobile device location, based in part on the person-to-device proximity indications in the broader task of generating occupancy estimates for one or more regions of a building and operating output devices based on occupancy estimates. For example the central controller could be located in a smartphone, thereby enabling the smartphone to play music on a dynamic subset of the wireless speakers located throughout a building, depending on the location of the user. The subset of speakers could be chosen based in part on weighting person and device location estimates, whereby the weights can be based in part on the proximity indication. When the person is close by the wireless device, device location is heavily weighted in selecting the subset of wireless speakers and when the person is determined to be non-proximal to the smartphone, occupancy indications form fixed wireless sensors receives increased weighting.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of provisional patent applicationSer. No. 62/054,389, filed on Sep. 24, 2014 by the present inventor.

BACKGROUND

The following is a tabulation of some prior art that presently appearsrelevant:

U.S. Patents

Pat. No. Kind Code Issue Date Patentee 8,577,392 B1 2013 Nov. 5 Pai etal. 8,102,784 B1 2012 Jan. 12 Lemkin et al. 7,843,333 B2 2010 Nov. 30Angelhag et al. 4,567,557 A 1986 Jan. 28 Burns 8,525,673 B2 2013 Sep. 3Tran

U.S. Patent Application Publications

Publication Nr. Kind Code Publ. Date Applicant 20150181405 A1 2015 Jun.25 Dua et al. 20110218650 A1 2011 Sep. 8 Crucs et al. 20110231020 A12011 Sep. 22 Ramachandran et al.

FIELD OF THE INVENTION

This disclosure relates generally to wireless building automationcontrollers and more specifically to the application of proximityindications (person-to-device proximity) by a central buildingcontroller for the purpose improving occupancy estimates in regions of abuilding and the control of electronic devices.

BACKGROUND

Recent advancements in building automation and wireless sensors havemade it possible to network whole building installations of previouslyautonomous devices (e.g. lights, wireless speakers and thermostats andhousehold appliances). In many cases it is inefficient and undesirableto operate all of the devices at once (for example turning on all thelights when a person arrives home). Consequently, an active problem inbuilding automation is to select a subset of devices (light, speakersetc.) that provide optimal performance, and dynamically adapts as theuser moves throughout the building. For example, in the case of anetwork of wireless speakers, an automated system could endeavor totrack a listen from room to room with music, such that the personreceives approximately constant volume as they move about.

Controlling networks of building based devices in accordance with thedynamically changing locations of people or the occupancy of regions ofa building is an ongoing challenge. Modern homes and buildings cancontain a multitude of short range wireless devices associated with thebuilding (e.g. motion sensors or a WiFi enabled television). These areoperable to transmit occupancy data about a region of a building.Central building controllers have been previously disclosed as devicesto aggregate sensor data from a variety of building based sensors anddevices (e.g. sensors, door locks, etc.) and to provide dynamic controlover a subset of client devices (lights, speakers, screens, door, andappliances).

Several occupancy detection technologies (e.g. motion detectors,infrared, ultrasonic and sound detection) have been used by centralcontrollers to determine occupancy of regions in buildings and controlsubsets of devices (e.g. automated lighting). However these technologiessuffer from several drawbacks. One problem is that sensors such as PIR,motion, ultrasound or sound sensors are more effective at sensing themovement of a person rather than their location or identity. Anotherproblem is that pets, trees and changes in heating and air-conditioningsystems are known to cause false positive readings. Later, the additionof mobile wireless devices enabled indoor location systems totriangulate and estimate mobile device location. U.S. Pat. No. 8,102,784and U.S. Pat. No. 7,843,333 describes systems to determine the locationof mobile wireless sensors in a network of fixed wireless sensors withknown position. The drawback with this approach for detecting theoccupancy in a region of a building is the implicit assumption that theperson and the mobile wireless device are always co-located.

More recently, mobile consumer wireless devices (smartphones, tabletsand smartwatches and Bluetooth beacons) have been used as mobilelocation beacons. This is due in part to the inclusion of short rangewireless transceivers (e.g., Bluetooth, BLE, WiFi and Zigbee). In manyhomes there is now a multitude of mobile wireless devices. Mobilewireless devices have considerable potential to improve occupancydetection and person location by central controllers.

U.S. Pat. No. 8,577,392 discloses a relay server that infers thelocation of a person, based on direct user-input activity at a pluralityof devices associated with the person. U.S. Pat. No. 8,577,392 disclosesdevices associated with a person (e.g. personal wireless electronics)but does not account for the wide variety of fixed position wirelesssensors, operable to indicate occupancy, associated with a building andnot associated with a person (e.g. motion sensors and door sensors).U.S. Pat. No. 8,577,392 states mobile phones can be a strong predictorof people's location. While it is often the case that a person is in thesame building as their mobile phone, this information is of very limiteduse to a central building controller, tasked with estimating real-timeoccupancy of various regions of the building. People often leave theirphone and personal electronics (e.g. tablet PCs) in one room and travelto another, particularly in homes and offices. Thus mobile devicelocation is an inconsistent predictor of people's location at the sizescale needed for effective building automation.

Consumer wireless devices are often left unattended for periods of timeand can therefore report misleading information regarding personlocation to a central controller. For example a person may arrive homeand the position of their mobile phone may provide accurate indicationof their location for a short period of time. Later the person may leavetheir mobile phone in one room and the mobile phone can reportmisleading occupancy data to the central controller. Thus the problem ofautomated control of electronic devices in response to a person'schanging indoor location remains largely unsolved. In particular, nocentral controller previously disclosed has effectively combined fixedwireless sensor data with mobile wireless device location data, whileaddressing the erratic reliability of mobile device location data, tothe task of determining the occupancy of regions of a building.

SUMMARY

Traditional building-based occupant detection systems (e.g. motionsensors and door sensors) have limited capability to estimate preciseoccupant location, or the identity of a person (e.g. select an identityfrom among known family members). Correspondingly building-basedoccupancy detectors have limited success controlling output devices(e.g. lights) in a timely or personalized manner (e.g. the familiarproblems of motion based lights turning off in a crowded room). Mobilewireless devices (e.g. smartphones and tablet PCs) are often associatedwith a primary user but have a wide range of accuracy for reporting auser's actual location, depending on usage patterns. The recentproliferation of short range (<400 meter) wireless messagingtechnologies (e.g. Bluetooth, WiFi and NFC) in consumer wireless deviceshas coincided with a growing number of sensors (e.g. temperature, lightlevel, vibration, acceleration, sound) in these mobile wireless devices.Future smart buildings can benefit greatly from timely and accurateperson location indications and hence the data fusion of fixed andmobile wireless devices in important.

According to the various embodiments of the present disclosure a centralcontroller can receive first signals containing indications of aspectsof a person from a plurality of fixed location short range wirelesssensors and second signals from one or more mobile wireless devices. Thecentral controller can receive first signals containing data indicativeof the occupancy of one or more regions of a building from a pluralityof fixed wireless sensors associated with a building, can obtain secondsignals from one or more mobile wireless devices, can weight a mobiledevice location estimate based on the estimated proximity of a person tothe mobile wireless device and can transmit third signals to one or morethird devices. Second signals can contain a mobile device locationindication and an indication of the proximity of the mobile wirelessdevice to a person. An aspect of several embodiments is to provide meansfor weighing the relevance of mobile device location estimates in theprocess of estimating the occupancy of regions of a building andcontrolling aspects of third devices (e.g. lights, speakers, thermostatsor appliances)

For example a central controller can receive first signals from aplurality of fixed wireless sensors (e.g. motion sensors, door sensorsor sound sensors), wherein the first signals contain data indicative ofthe occupancy of one or more regions (e.g. rooms, hallways orstairwells) of the building. The central controller can also receivesecond signals from a plurality of mobile wireless devices (e.g.smartphones, smartwatches or tablet PCs), wherein the second signalscontain data indicative of the location of the mobile devices and dataindicative of the degree of proximity each device has to a person (e.g.orientation data indicating that a device is handheld or horizontalindicating placement on a flat surface). The central controller canaggregate and estimate mobile device proximities and locations and cangenerate weights for at least some of the mobile device location databased on the proximity estimates, thereby generating proximity-weightedmobile device location estimates. In an aspect of several embodimentsthe central controller can combine fixed sensor occupancy data with theproximity-weighted mobile device location estimates to generate anoccupancy estimate for one or more regions of the building and in someembodiments estimate the identity of a person associated with occupancydata.

The proximity indication can identify circumstances when the centralcontroller should apply a high weighting to a particular mobile devicelocation estimate in the process of estimating the occupancy of a regionof the building. In other embodiments a central controller can use theproximity indications in second signals to generate weights for both theoccupancy data from fixed wireless sensors and mobile device locationindications. In another aspect of several embodiments the centralcontroller can select one or more third output devices (e.g. wirelessspeakers, lights, displays) and transmit signals to the selected thirddevices based on the occupancy estimates.

In several embodiments this disclosure enables a central controller torapidly transition between reliance on building-based sensor occupancyindications and mobile device location estimates, for the purpose ofestimating the occupancy of one or more regions of the building andsignaling automation devices.

The central controller can be located within the mobile wireless device.In this case the challenge for the controller is determining how toweight the relative importance of the mobile device location and fixedwireless sensors occupancy data to most accurately estimate occupancy.The disclosed system provides means for weighting the device and personlocation data, based in part on the proximity indication. For examplethe central controller could be located in a tablet PC, thereby enablingthe tablet PC to play music on a dynamic subset of the wireless speakerslocated throughout a building. Speakers can be chosen based on theestimated location of the primary user. The subset of speakers could bechosen based on part on weighting person and tablet PC locationestimates, whereby the weighting is based in part on the user proximityindication measured by the tablet PC.

Accordingly several advantages are that a user could start playing musicon wireless speakers in one room, walk to another room and the musicwould follow the user to the other room. The system provides improvedestimation of user location, by providing means to determine when mobiledevice data is relevant to the user location estimation calculation. Forexample, if a user began playing music from a tablet PC on wirelessspeakers and then put the tablet PC on a flat surface before going tothe kitchen the system provides means to identify that the sound shouldfollow the person and not remain clustered about the tablet PC. Thiswould eliminate the potential problem of playing music on all thespeakers at once.

In another example, a smartwatch can report location and proximityindications to a central controller (e.g. home router), whereby theproximity indications can be used to estimate that a person is wearingthe smartwatch while walking. The disclosed central controller can applya larger weighting to the smartwatch location data (e.g. relative tofixed occupancy sensors or an unattended smartphone).

Various embodiments of this invention have the potential tointelligently and efficiently control devices based on an occupancyestimate for one or more regions of a building and in some embodiments acorresponding identity estimate. Weighting the relative importance ofmobile device location data based on an indication of proximity of thedevice to a person can improve the accuracy of occupancy estimates,particularly in situations wherein fixed wireless sensors report weakindications of occupancy due to a lack of motion or sound. For example,in one embodiment a central control can identify that first signalsindicate the motion of a person and second signals contain a mobiledevice proximity indications (e.g. a horizontal device orientation),identify that the mobile device is not in motion (e.g. left on a table),and thereby enabling the weighting of the mobile device to be rapidlyreduced in the process of determining room occupancy or person location.

In another embodiment of the present technology, identifying a personresponsible for occupancy indications can greatly customize futurelocation predictions and the selection of third devices. A centralcontroller can contain or access one or more historical user profiles,each associated with a particular person who occupies the building. Thecentral controller can generate one or more identity estimates based onoccupancy indications in one or more regions of the building. Thecentral controller can weight a common aspect of one or more historicaluser profiles, where the weights can be selected based on strength ofeach identity estimate. The central controller can select third devicesbased in part on the combination of identity-weighted historical profileaspects. New occupancy data or patterns of behavior can be assigned to ahistorical user profile if the identity of the person responsible foroccupancy estimate can be determined with sufficient confidence. Eachhistorical user profile can contain information on associated mobiledevices and occupancy patterns, including their favorite locations,common paths traveled in the building and dwell times.

In another aspect of the present technology the historical user profilecan contain a location correlation matrix. Each element of the locationcorrelation matrix can indicate the probability that the presence of theperson in a first region will correspond with occupancy indications in asecond region at some time later. A central controller can estimate theidentity of a person responsible for occupancy data transmitted from oneor more fixed wireless sensors. The historical user profilecorresponding to the estimated identity can be used as part of theprocess to select one or more third devices. In yet another aspect ofthe present technology the historical user profile corresponding to anestimated identity can be used to select third devices in anticipationof the future arrival of the person corresponding to the estimatedidentity. In yet another aspect of the present technology, a centralcontroller can generate an occupancy estimate one or more regions of abuilding, can then calculate multiple estimates for the identity of theperson (e.g. a probability for each family member based on analysis ofsensor data or mobile wireless device interactions). The system canweight a common aspect from multiple historical user profiles (e.g.favorite locations for each member of a family), whereby weights arebased in part on the corresponding identity probability. The centralcontroller can select one or more third devices, based in part on theidentity-weighted aspects from a plurality of historical user profiles.

Advantages

The techniques described in this specification can be implemented toachieve the following exemplary advantages: Person location accuracy canbe improved by rapidly identifying relevant mobile devices that areproximal to people.

Identities can be associated with occupancy data generated by homeautomation sensors (e.g. motion sensors), by correlating patterns offixed wireless sensor data with mobile device locations, in particularwhen there is a strong proximity indication between a person and themobile device. For example, motion in a home-office may sometimes beassociated with strong person-to-device proximity indication atsmartphone A but never with smartphone B. Subsequent fixed sensor datain the home-office can be associated with the primary user of smartphoneA, thereby enabling the association of fixed sensor occupancy data witha person.

The disclosed system provides means to reduce false negative indicationsof room occupancy. This is a particular problem with motion activatedlight, wherein previous technologies often mistake periods of inactivityas an unoccupied region. For example, a central controller can receiveweak occupancy indications from fixed motion sensors if a person issitting quietly for a period of time. The disclosed central controllercan estimate mobile device locations and determine person-to-deviceproximity and thereby keep the lights on during inactive periods.

Similarly, if a mobile device in another room experiences a rapidincrease in person-to-device proximity, the disclosures provides meansfor this indication to act as confirmation that a person has left theoriginal location and cause the lights to turn off, thereby savingenergy and reducing latency and false negative events.

The disclosed central controller can reduce power consumption andnetwork traffic. In one embodiment occupancy indications from fixedwireless sensors associated with a building can cause the centralcontroller to instruct mobile wireless devices to transmit secondsignals including location and person proximity indications. Similarly,frequent transmission of location data from mobile devices can causesignificant power consumption. The disclosed controller can estimate theproximity of a person to a mobile device and thereby identify devicesmost indicative of the person's location. This approach improves powerconservation by reducing transmission frequency from mobile nodes thathave low person-to-device proximity. In some embodiments, a mobiledevice can use a large or rapid change in person-to-device proximity toinitiate location transmission, thereby further conserving power.

The disclosed system can improve occupancy estimation for elder care. Aplurality of fixed wireless sensors (e.g. motion) can monitor a person'smobility and occupancy. The mobile wireless device can be a home helppendant (e.g. Life Alert) and the proximity indication can be the falldetection technology. The disclosed system enables improved mobility andfall detection by weighing the pendant location data based in part onthe wearer's proximity indication and the fixed sensor data. For examplethe pendant location can receive a low weighting if it indicates astationary horizontal placement (e.g. placed on a nightstand) and fixedmotion sensors indicate regular motion patterns elsewhere. Conversely,the central controller can apply a high weighting to the pendantlocation data if the proximity indications show stationarynon-horizontal placement (e.g. a person lying on a floor) and fixedmotion sensors indicate abnormal motion patterns. In some embodimentsappliances (e.g. refrigerators) with wireless capability (e.g.Bluetooth) can be fixed wireless sensors operable to indicate occupancybased on user activity (e.g. opening the refrigerator door). Suchappliances can further indicate regular or irregular occupancy patternsfor regions of a building.

In some embodiments the central controller can use the proximityindications in second signals to enhance the accuracy of the mobiledevice location estimates. For example a mobile phone may transmitlocation data to a central controller and may also report proximity datain the form of device orientation measurements (e.g. pointing North withan elevation angle of 70 degrees above the horizon). The centralcontroller can use the person proximity indications to improve mobiledevice location estimates by accounting for factors such as theplacement of the person relative to the mobile device or the orientationof the transmitting antenna on the mobile device. In this case theweight generated for the mobile device location data can be in the formof a correction factor.

The disclosed central controller enables improved estimation of thenumber of occupants in a building and regions occupied in a building.For example, a system that accepts all reported mobile device locationscan result in overestimating occupancy. Conversely, a system thatestimating occupancy based on only those mobile devices that reportdirect user interaction (e.g. typing or touching a screen) canunderestimate occupancy. Embodiments of the disclosed central controllercan improve occupancy estimation by weighing mobile device locationsbased on a variety of close range and long range person proximityindications (e.g. hearing background voices, or sensing vibration from aperson typing nearby), included in second signals from mobile wirelessdevices.

The disclosed central controller enables fixed wireless sensor data tobe associated with a specific mobile device user, by enablingcorrelation between fixed wireless sensor data and proximity-weightedmobile device locations. One use of this advantage is in subsequentsituations where only fixed sensor data is available (e.g. no mobiledevices are present), whereby the central controller can use a locationmodel to add historical context, thereby enabling location and identityto be estimated based on times when mobile devices were present. Forexample when a person is in a room with no mobile devices, the systemcan estimate their location based in part on previous instances withsimilar fixed sensor readings, when a mobile device with estimatedlocation, was reported as being in close proximity to the persons.

The system enables mapping of individual users occupancy patterns. Thisfurther enables the system to take user specific actions such asdelivering personalized music or news reports. For example a messagedelivered to the central controller can be routed to a home automationdevice (e.g. video screen) based on a reported smartphone location andan indication of strong proximity (e.g. the phone is at an angleindicating it is being carried)

Another advantage is that the central controller can improve reportingof a person's last known location (e.g. in emergency situations). Thecentral controller can estimate and store person-to-device proximityestimates, thereby providing improved determination of the last knownlocation of a person. For example, prior to an earthquake, the systemcould receive location and proximity indications from a plurality ofpersonal electronic devices and identify that a person was proximal totheir smartphone (e.g. snoring nearby) and not proximal to their tabletPC, or smartwatch. The system could also identify one or more mobiledevices that are most indicative of proximity to a person prior to anevent.

Accordingly, several advantages additional advantages of the disclosedcentral controller with historical user profiles are as follows. Oneadvantage is improved future location prediction and automation deviceselection. The disclosed system improved future location prediction byestimating the identity of the occupant. Previous building automationcontrol systems do not differentiated between users when anticipatingfuture location based on historical data. Building occupants haveindividual patterns while aggregate data across all occupants has lesspredictive power. By estimating the identity of an occupant the presenttechnology provides means to group individual user patterns, therebyenabling improved future location prediction. For example if there arethree bedrooms at the end of a hallway, prior central controllers couldaggregate historical occupancy data for all users and estimate asubstantially equal likelihood that someone walking down the hallwaywill enter one of the three rooms and activate third devices (e.g.lights) accordingly. The present technology estimates the person'sidentity. In cases where the identity estimates clearly points to oneperson the lights in the corresponding bedroom can be raised in advanceof their arrival.

The disclosed system provides improved user experience through reducedlatency. One of the challenges with building automation systems is thetime delay between sensing a person and activating home automationdevices (e.g. the time lag between entering a room and automatedlighting activation). The disclosed technology reduces latency byanticipating a user's future location. In this way lights and music candynamically adapt to the users future location and be ready before theirarrival. The technology provides means to estimate a user's futurelocation or behavior based one generating one or more estimates of theiridentity and weighting aspects of one or more historical user profiles.For example using the present system if a person in an office buildingwalks down a hallway with several offices, the system could estimatetheir identity with 60% probability as being person #1 and who works inoffice #1 and 40% probability that they are the person #2 who works inoffice #2. The system could access the respective historical userprofiles, identify favorite destinations (e.g. office, or break room)associated with each identity estimate. The system could then, weightthe favorite locations in accordance with the respective identityprobability. The system could then light the path to offices #1 and #2.At some time later, additional sensor or occupancy data can confirm oneof the identity estimates (e.g. person enters office #1) and lightinglocations associated with low probability identity estimates can bedimmed.

In several embodiments the disclosed central controller enables improvedreal-time occupancy estimates for discrete regions of a building thatcan be provided as core functionality in a building operating system.Persons of skill in the art can develop applications that utilize theenhanced occupancy estimates to automate devices and realize a widevariety of improvements including energy savings, customizedentertainment and safety.

DRAWINGS

FIG. 1A and FIG. 1B are exemplary diagrams of an indoor area in which acentral controller receives signals from mobile wireless devices andfixed wireless sensors, according to an embodiment of the presentdisclosure.

FIG. 2 illustrates an exemplary fixed wireless sensor in accordance withone embodiment of the present disclosure.

FIG. 3 illustrates an exemplary mobile wireless device in accordancewith one embodiment of the present technology.

FIG. 4A, FIG. 4B, FIG. 4C and FIG. 4D are functional diagrams ofexemplary central controllers according to several embodiments of thepresent disclosure.

FIG. 5 illustrates a flow diagram of a process for generating andtransmitting first signals from a fixed wireless sensor according to anaspect of the technology.

FIG. 6 illustrates a flow diagram of a process for generating andtransmitting second signals from a mobile wireless device according toan aspect of the technology.

FIG. 7 illustrates a flow diagram of a process wherein a centralcontroller chooses one or more third devices, according to an aspect ofthe technology.

FIG. 8 illustrates a flow diagram of a process wherein a centralcontroller chooses one or more third devices, according to an aspect ofthe technology.

FIG. 9 is a functional diagram of two location correlation matricesbeing combined with profile weights to form a weighted locationcorrelation matrix in accordance with an embodiment of the presentinvention.

FIG. 10 illustrates a flow diagram of a process wherein a centralcontroller chooses one or more third devices, according to an aspect ofthe technology.

DETAILED DESCRIPTION

FIG. 1A illustrates a system 100, within a building 115, for dynamicallycontrolling one or more automated electronic devices 110, according toone embodiment of the present disclosure. In the embodiment of FIG. 1central controller 150 receives first wireless signals 140 from aplurality of fixed wireless sensors 120. Central controller 150 furtherreceives second signals 185 from two mobile wireless devices 130 a and130 b. Mobile devices 130 a and 130 b can determine their respectivelocations based on wireless localization signals 165 from referencelocations (e.g. a fixed wireless sensor 120 c, a wireless access point180, a GPS satellite 175 or a cellular network tower 170). In thisembodiment mobile devices 130 a and 130 b can generate proximity dataindicative of their respective proximities to a person 152. Theproximity data can be based on direct user input (e.g. typing ortouching the screen) or indirect user input (e.g. the sound of peopletalking in the background, temperature changes, elevation angle orvibration). Mobile wireless devices 130 a and 130 b can transmitlocation and proximity indications as second data in second signals 185.In the embodiment of FIG. 1 central controller 150 weighs the mobiledevice location indications based in part on the proximity indications.Central controller 150 combines weighted mobile device locationindications with occupancy data from first signals 140 and generates anoccupancy estimate for two regions 116 a and 116 b of indoor area 115.In the embodiment of FIG. 1 the regions 116 are rooms within thebuilding. The term “region” is used herein broadly to mean a portion ofa building operable to be occupied by one or more people. Regions can bemultiple rooms, portions of a room or a range of points on a Cartesiancoordinate system (e.g. a region can be all points on an XYZ gridrelative to some origin point wherein X is between 0 and 3, Y is between0 and 10 and Z=1). Regions can be substantially non-overlapping asillustrated by regions circular regions 111 a and 111 b in FIG. 1B. Inthe context of this disclosure a region is considered “non-overlapping”when the region that does share more than 50% of it's occupiable areawith another region. A region can be part of a set of regions thataccounts for some or all of the occupiable space in the building. Forexample, a central controller can contain a set of 5 regions,established at the time of installation to correspond to 5 rooms thatcontain wireless occupancy sensors (e.g. a set of regions={ENTRYWAY,KITCHEN, BEDROOM, LIVING ROOM, BATHROOM}). In some embodiments, centralcontroller 150 can maintain a current occupancy estimate for some or allregions in the set. In other embodiments system 100 can contain a singleregion that encompasses some or all of the building. For example, acentral controller can be operable to automatically turn on a televisionwhen occupancy is sensed in a living-room.

Central controller 150 can dynamically select one or more third devices,for example 110 a and 110 b, based on the occupancy estimates andtransmit third signals to selected third devices. Third devices 110 areoperable to receive third signals 190 from a central controller andalter an aspect of their operation based in part on third signals. Thirddevices 110 can be building-based automation devices include lights,climate controllers, security systems, appliances and wireless garagedoors. Third devices 110 can be building-based media distributiondevices, speakers, music systems television tuners, wireless routers anddigital video recorders. Third devices 110 can be mobile wirelessdevices including smartphones, music controllers, tablet PCs,smartwatches, autonomous vehicles, robots or drones. Third devices canalso be computers or automobiles.

In some embodiments the proximity and location indications are notincluded as second data but instead can be aspects of the second signalquality (e.g. received signals strength indication, bit error rate,signal-to-noise ratio or time of flight delay). For example a mobiledevice may report to a central controller that it is stationary, whilethe variations in the received signals strength indication (RSSI) ofsignals 185 received at the central controller 150 can indicate thepresence of a person close to the mobile wireless device. In anotherexample the mobile device location indication may be based on thereceived signal strength indication.

In some embodiments the mobile device location indication in secondsignals may be based on receiving first signals 140 at a mobile wirelessdevice. For example, the location indication in a second signal can beRSSI measurements based on a plurality of first signals 140, received atboth a mobile device and the central controller 150. The mobile device130 a or 130 b can relay the received RSSI values to central controller150 in second signals. The central controller can compare RSSI valuesfrom first signals with those received as location indications in secondsignals and thereby estimate the location of the mobile wireless device.

In yet other embodiments occupancy data from a plurality of fixedwireless sensors can be combined t the central controller 150 togenerate an occupancy estimate in a smaller region. For example firstdata from fixed sensors 120 a and 120 b can indicate that person 152simultaneously occupies a location within the circular regions 111 a and111 b. Central controller 150 can estimate that a person 152 inoccupying the overlapping region 112.

In some embodiments central controller 150 can be a standalone fixedlocation wireless device (e.g. a Wireless router, a wireless accesspoint, a wireless repeater or a computer with wireless capability). Inother embodiments the central automation controller 150 can be locatedinside a mobile wireless master device 155. Examples of a wirelessdevice that could contain a central automation controller and embody thewireless master device 155 include a home entertainment controller (e.g.TV, lighting or audio controller), a mobile media server, a tablet PC,smartphone or smartwatch).

Fixed Wireless Sensors

Central controller 150 receives first wireless signals 140 from one ormore fixed wireless sensors 120, associated with a building. Fixedwireless sensors 120 can sense occupancy in a region around the sensor(e.g. an omnidirectional region around the wireless sensor based on aneffective operating range or a region in a directional field of view)and can have little or no specificity to or association with a person.The term “fixed wireless sensor” is used herein broadly to mean anelectronic device or portion of a device with means of sensing one ormore aspects of a person in a region of the building, including directuser interaction (e.g. pushing a button or moving a lever) andtransmitting short range wireless signals containing occupancy data to acentral controller 150. Fixed wireless sensors 120 can be attached to abuilding structure or have transitory placement within a building or ona building exterior (e.g. external security camera or motion detector).

Fixed wireless sensors 120 can have one or more sensors operable tosense an aspect of a person in the vicinity of the sensor. Examplesinclude wireless home security sensors such as infrared motiondetectors, magnetic proximity sensors, sound detectors, room entry andexit detectors, baby monitors, ultrasonic motion sensors and wirelesssecurity cameras. Other types of fixed wireless sensors 120 can reportoccupancy based on user interaction such as a button push or breaking anelectrical contact, for example wireless window sensors, door sensors,door locks and a security system keypad.

A Fixed wireless sensor 120 can be part of a larger electronic appliance(e.g. a wirelessly enabled refrigerator, television, coffee maker, doorlock or washing machine). Many manufacturers are now installingBluetooth, Zigbee and WiFi transceivers in these appliances for thepurpose of reporting occupancy based on direct or indirect userinteraction. Another example of a fixed wireless sensor is the AmazonEcho speaker available from Amazon Inc. of Seattle Wash. The Echospeaker contains a plurality of microphones and can transmit short rangeBluetooth and WiFi signals containing occupancy indications.

In some embodiments a wireless sensor 120 can be part of an automationdevice 110 with wireless control capability (e.g. wireless sensor node120 a is housed in a common electronics enclosure with a wirelessautomation device 110 a). Automation device 110 a can be operable toreceive third signals 190 from central controller 150. The combinationof the sensor and wireless automation device can share a transceiver andantenna for sending first signals 140, including status and state of theautomation device 110 a, and receiving third signals 190. FIG. 2illustrates an exemplary fixed wireless sensor 120 in accordance withone embodiment of the present disclosure. Each fixed wireless sensor cancontain one or more sensors 200 which can sense an aspect of a person.Sensors can include passive infrared (PIR) and active infrared sensorssuch as found in security systems as well as thermal, sound, light orvibration sensor. The sensor may also include a digital camera. Fixedwireless sensors can also sense state changes such as the operation ofbuttons 205 a or actuators 205 b (e.g. the lever indicating therefrigerator door has been opened). Fixed wireless sensors can contain ashort-range wireless transceiver 210 and one or more antennas 205,operable to transmit first signals 140 to central controller 150.

First signals 140 can contain sensor measurements indicative of anaspect of a person (e.g. motion, thermal, sound, humidity or vibrationassociated with a person). In some embodiments a first signal 140 caninclude a unique identifier, used to identify a fixed wireless sensor120 relative to other wireless sensors in the system.

Exemplary fixed wireless signals 140 include electrical, acoustic oroptic signals. A short range wireless transceiver 210 is considered tohave an operable transmission range less than 400 meters. Exemplaryshort range electrical protocols include Z-wave, Zigbee, Bluetooth,Low-Energy Bluetooth (BLE), WiFi, RFID. Examples of short range opticalprotocols include IEEE 802.15.7 and Infrared Data Association (IrDA).The wireless transceiver 210 and signals generator can be embodied asone or more microchips for example the CC3000 WiFi transceiver andapplication processor or CC2430 Bluetooth transceiver and applicationprocessor, both available from Texas Instruments of Santa Clara, Calif.The wireless transceiver 210 and signal generator 230 can perform one ormore pairing operations operable to associate a fixed wireless sensor120 with a specific central controller 150. Examples of pairingoperations include, responding to an advertising signal from a centralcontroller, negotiating or transmitting a unique ID for a fixed wirelesssensor (e.g. session ID associated with short range wireless messages),establishing a secure connection by agreeing on a message cypher with acentral controller, announcing to a central controller the capabilitiesof a fixed wireless sensor (e.g. motion, vibration, orientation, sound,humidity, state and number of buttons) or announcing capabilities suchas data rate or the model number identifying the specific type of fixedwireless sensor. The pairing operation can associate a fixed wirelesssensor 120 with a central controller 150.

Fixed wireless sensors 120 can contain a signal processor 240 coupled tosensor 200, buttons 205 a or actuators 205 b, to perform various degreesof processing on the raw sensor data. The overall function of theprocessor can be to highlight sensor or actuator readings indicative ofthe presence of a person in a region around the sensor and generatefirst data. The processor can function to remove background sensorreadings, detect peak values, filter the data or otherwise process thesensor data. For example, acoustic sensor readings may be filtered toremove background noise, or sounds known not to indicate the presence ofa person such as the hum of refrigerator motor or the rustling of trees.The signal processor may thereby function to produce first data whichhas enhanced or highlighted indication of occupancy in a region aroundthe fixed wireless sensor. The processor may combine data from multiplesensors or employ complex pattern recognition algorithms and outputfirst data indicative of occupancy of a region around the fixed sensor.The signal generator 230 can combine sensor data and a unique identifierusing the wireless protocol 220 to generate first signals 140. Theunique identifier can serve to identify the fixed wireless sensor andidentify first signals 140 as coming from a particular fixed wirelesssensor 120. First signals 140 contain first data. Examples of first datainclude raw sensor values and values indicating occupancy probability(e.g. 0.9 or 90%). In some embodiments fixed wireless sensor 120 cansegment the field of sensing into smaller regions and report occupancydata for each of these smaller regions. For example a wireless cameracan transmit raw images, compressed images or images processed toidentify occupancy in specific smaller regions as first data. The regionfor which a fixed wireless sensor is operable to report occupancy can beidentified at the time of installation. For example, a security systemcan be setup such that specific wireless sensors cover specific regionsof a house (e.g. front hallway, master bedroom or kitchen). Theseregions can identified to the central controller, thereby enabling firstsignals associated with a wireless sensor to be associated with apreviously defined region (e.g. room, hallway, or portion of a room). Insome embodiments fixed wireless sensors may not identify the region inwhich they are operable to sense occupancy and instead the centralcontroller can define regions based on processing first signals. Forexample a central controller 150 can aggregate or process first datafrom a plurality of fixed wireless motion sensors 120 and identifyrelationships (e.g. correlated first data, sequential patterns,coincident first data or overlapping regions) and thereby identifyregions associated with occupancy data from each fixed wireless sensor.Fixed wireless sensor 120 can include a signal requestor 250 operable toreceive short range wireless signals from other fixed sensors or acentral controller and initiate the transmission of a first signal 140.

Mobile Wireless Devices

FIG. 1 illustrates two mobile wireless devices 130 a and 130 b. Mobilewireless device 130 a is embodied as a smartphone and 130 b is embodiedas a tablet PC. Embodiments of the disclosure may be implemented with awide variety of mobile devices with short range wireless transmitters ortransceivers. The mobile wireless device can be e.g. a mobile phone, atablet PC, a laptop computer, a smartwatch, a wearable health monitorsuch as FitBit™, for FuelBand™ or a portable media player. The mobilewireless device may also be a wireless tag, whereby the primary purposeis to aid system 100 to locate a person 152 or a device attached to thetag.

The mobile devices 130 a and 130 b can be associated with a primaryuser. Examples of device-to-user association include a smartphone or atablet PC assigned to an employee in an office, a smartwatch or healthmonitor primarily worn by one person or a portable laptop computer usedby one family member. In some cases one person may be the primary userof a device at one time e.g. in the morning, while another person may bethe primary user at another time e.g. at night.

FIG. 3 illustrates an exemplary mobile wireless device in accordancewith one embodiment of the present technology. The mobile device 130 cancontain one or more wireless transceivers 305 operable to transmitsecond signals 185 to central controller 150. Mobile wireless device 130can also include one or more antennas 307. Antenna 307 can be a chiptype antenna, a wire or can be printed on a printed circuit board.

Device Location Estimator

Mobile device 130 can include a device location estimator 315 thatestimates a past or current location of mobile wireless device 130.Device location estimator can send data about the location of the mobiledevice to the signals generator 330. Device location estimator 315 canbe implemented as one or more processors or one or more integratedcircuited. Device location estimator 315 can be operable coupled to avariety of signals receivers operable to receive localization signals165. Device location estimator 315 can perform various degrees ofprocessing to estimate the mobile wireless device location. Localizationsignals 165 can be short range wireless signals (e.g. WiFi, Bluetooth,Zigbee) or long range wireless signals (e.g. GPS, 3G/4G cellularsignals). Analysis of localization signals 165 can indicate externaltransmitters (e.g. cellular towers or WiFi hotspots) operable to providethe most accurate localization estimate. For example, the analysis canidentify one or more Bluetooth beacons, fixed wireless sensors 120,automated electronic devices 110, cell towers 170, GPS satellites 175 orWiFi access points 180. Each external device can be associated with aknown location, such that a location of the mobile device 130 can beestimated, e.g., via a triangulation or a trilateration technique.

Device location estimator 315 can process wireless signals received bythe second data transceiver 305 or another receiver associated with themobile device 130. For example, device location estimator can beoperably coupled to a GPS receiver 317 b that receives GPS signalsidentifying GPS satellites and can use received GPS signals to estimatemobile device location. In another example mobile device locationestimator 315 can include a cell-tower detector 317 a that detects whichcell tower or cell towers are carrying cellular communicationsassociated with mobile device 130. In another example, mobile devicelocation estimator 315 can include a WiFi detector 317 c that detectsnearby WiFi transmitters. The transceiver 305 can be operable to receivefirst signals from the fixed wireless sensors 120. The mobile devicelocation can be calculated based in part on an aspect of the firstsignal 140 e.g. received signal strength, time delay between sensing andreceiving the signal or the bit error rate of the first signal 140.Device location estimator 315 can perform various degrees of computationincluding estimating distance from a mobile wireless device 130 to fixedwireless sensors 120, position triangulation and position trilateration.Device location estimator 315 can use third signals 190 or other signalsfrom the central controller to estimate the location of the mobiledevice (e.g. within a range of 10 meters of the central controller)Device location estimator 315 can generate and transmit mobile devicelocation data to a signal generator 330. Mobile device location data canbe one or more aspects of localization signals 165, for example receivedsignals strength, or the order of signals received (e.g. time of flightused by GPS systems). Mobile device location data can indicate theresult of a calculation by device location estimator 315 indicating theestimated distance from the mobile device to one or more referencepoints (e.g. 10 meters from the central controller). A mobile devices orcentral controller can have a plurality of antennas operable to transmitindependently or in combination (e.g. a MIMO antenna array). For examplemobile device location data can be an indication of the antennareceiving a localization signal 165 or third signals 190 with greatestsignals strength. Mobile device location data can include an indicationof the direction of the mobile device relative to a reference location(e.g. location data can indicate the mobile device is southwest of thecentral controller).

Mobile device 130 can transmit second signals 185 containing a mobiledevice location indication and a person-to-device proximity indication(e.g. proximity data 325). Proximity data 325 can be gathered from awide variety of proximity sensors 310, can be processed by a dataprocessor 320 to further highlight proximity indications and canintegrated into second signals 185 by signals generator 330.

The mobile phone 130 a is shown proximal to the person 152. The mobiledevice 130 b is not proximal to a person, such as a tablet PC that hasbeen left on a table 160 in an unoccupied room for a period of time.Examples of proximity include devices attached to a person or placedclose by a person, such as on a work desk, nightstand, kitchen counter,coffee table or car seat. Proximity can be indicated by the mobiledevice sensing acoustic signals, later determined to by typing, snoringor moving about a room. Further examples of proximity include being heldduring user-input, being carried in a person's hand, pocket, purse,backpack, or briefcase. In general, a mobile wireless device 130 can beconsidered to be proximal to a person when one or more sensors on themobile device are able to sense the presence of the person. Proximitybetween a mobile wireless device and a person can also be indicated byvariations in signal strength of signals 185 transmitted from a mobiledevice 130 to the central controller 150.

The proximity between a person and a mobile device can have a widevariety of values. For example a person engaged in direct userinteraction with a mobile device (e.g. typing or touching a screen) canindicate direct proximity. Direct proximity can indicate that a personis within reach of the wireless device (e.g. 1M). Other examples ofdirect proximity are vibrations indicative of walking with the mobiledevice in a pocket or purse, heartbeat measured by a mobile fitnesstracker and small variations in the angle of inclination of a tablet PCindicating that a user is reading from the screen.

The mobile device 130 can also contain a wide variety of sensors 310capable of sensing direct proximity as well as longer range indirectproximity (e.g. a tablet PC with a microphone that can hear a persontalking in the background). For example mobile device sensors mayinclude an accelerometer, microphone, gyroscope, magnetometer (digitalcompass), barometer, humidity sensor, altimeter or camera. Otherexamples include sensors capable of measuring temperature and lightlevel in the vicinity of the device. The function of the sensors 310 isto provide data indicative of the state of the device and indicative ofproximity of one or more people. Examples of data indicative of devicestate include, angular orientation of the mobile device relative to theground, direction the device is pointing (e.g., 330 deg NW.), vibration,acceleration and power state (e.g. active, standby or sleep). Examplesof data indicative of person proximity include sound measurements (e.g.hearing someone rustle a newspaper), vibration (e.g. associated withtyping on a keyboard), acceleration (e.g. consistent with a personwalking) or changes in received wireless signal quality (e.g. receivedsignal strength indication (RSSI) or bit-error-rate). For example,changes in signal quality can be indicative of a person momentarilyblocking or shadowing a transmitting source.

The mobile device 130 can contain a data processor 320 coupled to thesensors 310 to perform various degrees of processing on the raw sensordata. The function of the processor 320 can be to highlight importantsensor data while reducing the total amount of sensor data transmittedby the device. The processor can function to remove background sensorreadings, detect peak values, filter the data or otherwise process thesensor data. For example the acoustic sensor readings may be filtered toremove white noise, or sounds known not to indicate the proximity of aperson such as the hum of refrigerator motor or the rustling of trees.The signal processor may thereby function to produce output data whichhas enhanced or highlighted indication of person to device proximity.The processor may combine data from multiple sensors and output anaggregated indication of person proximity. In one example processor 320can be a digital signals processor operable to process a large number ofsound samples (e.g. 44100 per second), remove high volume backgroundnoise (e.g. cars or machinery) and highlight indications of speechindicative of the proximity of a person.

The signal generator 330 functions to combine the proximity data 325from the data processor 320 with location data from the locationestimator 315. The signal generator can use a communication protocol 340to form one or more second signals 185 which are transmitted by theshort range transceiver 305. In one embodiment the transmitter andreceiver use the same short range wireless protocol. In otherembodiments these could be different protocols. For example the wirelessdevice may receive Bluetooth signals from nodes and transmit WiFisignals including the mobile device location data and proximity data.

Central Controller

FIG. 4A is a functional diagram of a central controller 150 inaccordance with an embodiment of the present disclosure. Within theperson location system 100, the overall function of the centralcontroller 150 can be to select one or more automated electronic devices110 to operate, based on fixed wireless sensors data indicative of aperson's location, augmented with relevant mobile device locations. Toaccomplish this function the controller gathers signals from fixedwireless sensors and mobile devices, determines the relevance of mobiledevice location, based in part on the proximity of the device to aperson (e.g. strong or weakly indicative of a person close by) andtransmits signals to third devices based on an the estimated occupancyof one or more regions of the building. In other embodiments theproximity-based weightings are used to estimate the identity of a personresponsible for at least some of the fixed wireless sensor data and canselect aspects of third signals (e.g. the content or selected automationdevice) based in part on the identity estimate.

The central controller contains one or more receivers 402 and one ormore antennas 403 operable to receive first signals 140 from fixedwireless sensors 120 and second signals 185 from mobile devices 130. Insome embodiments receiver 402 is part of a transceiver that is alsocapable of transmitting signals. Receiver 402 can include suitablehardware for performing device discovery, connection establishment, andcommunication necessary to receive first signals 140 and second signals185. Receiver 402 can be configured to operate with a variety of shortrange wireless transmission protocols e.g. Z-wave, Zigbee, Bluetooth,Low-Energy Bluetooth (BLE), WiFi, RFID and IrDA. Exemplary wirelesssignals include electrical, acoustic or optic signals. It should also beunderstood that several other short-range receivers can be used that donot utilize any standardized wireless protocols (e.g., car keyfobs).Receiver 402 can be a single microchip transceiver operable to receivesignals, demodulate signals, identify data packets and transmit some orall of the data in a packet to other components (e.g. processors, memorydevices) within the central controller, using a transmission protocole.g. (I2C, UART, Ethernet or I2S). First signals 140 and second signals185 can be received using different receivers and different short rangetransmission protocols for example first signals can be Z-wave packetsand second signals can be low energy Bluetooth packets. Receiver 402 isalso operable to measure one or more quality aspects of the receivedsignals (e.g. RSSI, signal to noise ratio, time of flight and bit errorrate), convert these indications to signal quality data and transmitthese to other components within the central controller 150 along withpacket contents.

Central controller 150 contains an occupancy estimator 408, operable togenerate an occupancy estimate for one or more regions of the building.In FIG. 4A occupancy estimator 408 contains two data aggregators 404 aand 404 b operable to aggregate first and second signals. Fixed sensordata aggregator 404 a can gather occupancy indications (e.g. occupancydata or first signals quality indications) from first signals. Mobiledevice data aggregator 404 b can gather mobile device locationindications (e.g. location data or second signals quality indications)and gather proximity indications (e.g. proximity data or second signalquality indications) from second signals. Each aggregator can trackindicators such as the maximum value of data from a sensor over aparticular time window (e.g. peak signal within the previous minute).Data from multiple sensors from a single fixed wireless sensor can becombined (e.g. combining PIR and sound data to verify the presence of aperson or to estimate the distance of a person from the sensor) Thefunction of the aggregator can be to provide sufficient data andhighlight important data for subsequent processing steps. For example,in one embodiment, aggregated first data from 404 a can be inspected forindication of activity, occupancy or large changes in occupancy sensedby fixed wireless sensors 120. Central controller 150 can apply anactivity criterion to the aggregated first data. Central controller 150can assess the activity criterion and identify strong activity andoccupancy signals. In response occupancy signals or changes (e.g.satisfying an activity criterion) central controller 150 can transmitadvertising signals, operable to request mobile wireless devices torespond with second signals. Aspects of the advertising signal can beselected to reach some or all available mobile wireless devices (e.g.selecting the BLE advertising channels #37, 38 and 39). In this waycentral controller can avoid requesting second signals until activity issensed at fixed wireless sensors 120.

The occupancy estimator 408 can contain a mobile device locationestimator 420, operable to determine an estimate of a mobile devicelocation. Mobile device location estimator 420 can receive aggregatedmobile device location data from aggregator 404 a. Mobile devicelocation estimator 420 can combine and refine the mobile device locationdata, received in second signals and perform various degrees ofprocessing to estimate the mobile device location. Mobile devicelocation estimator 420 can use knowledge of network layout and placementof signal sources (e.g. routers, fixed wireless sensors, cell towers) toestimate the mobile device location, based on data contained in secondsignals 185. In some embodiments mobile device location estimatorutilizes a historical location model 412. The historical location modelcan be populated or trained with the floorplan of the building. Thelocation model can indicate all occupiable regions in the building.

In one aspect of several embodiments the mobile device locationestimator can estimate locations for mobile devices that are notproximal to people and thereby rule out these device location estimatesin the process of calculating an occupancy estimate for the building.The output from the mobile device location estimator 420 can be a regionof a building (e.g. the kitchen, a bedroom or a hallway), a portion of aroom (e.g. the left side of a home office) or distance relative to thecentral controller or other reference point (e.g. located within 10meters of the central controller) a location on a Cartesian coordinategrid or similar coordinate system.

In one aspect the mobile device location estimator can determine theregion 116 of the building corresponding to a mobile device locationestimate and identify this region as a mobile device location region.For example central controller 150 can receive second signals from twomobile wireless devices, and estimate the device locations at 420.Mobile device location estimator can further determine that thelocations fall within two regions (e.g. kitchen and bedroom) and canidentify the kitchen and bedroom as mobile device location regions. Incomparison, previous building automation controllers typically lack thespatial accuracy to assign a mobile wireless device to a region of thebuilding.

Central controller 150 can contain a location model 412. Mobile devicelocation estimator 420 can receive data from and conversely add data tothe location model 412 for the building. The location model 412 can bestored remotely from other components of the central controller. In someembodiments the location model can be stored on a remote server, alongwith many other location models and accessed by the central controller150 over a computer data network (e.g. World Wide Web, Ethernet or LAN).The location model 412 can contain historical location data for one,some or all people who have occupied a building as well as correlationdata relating sensor values and estimated locations. The model cancontain confidence data about previous occupancy estimates. For examplea region in which occupancy that has been confirmed by several fixed andmobile sensors can be assigned a high confidence value. The locationmodel can contain a map of all possible regions in a house, based onoverlapping historical locations for all occupants. In otherembodiments, the model can be based on data from other similar buildings(e.g. houses with a similar floor plan). In situations where theidentity of a person can be determined, the model can associatehistorical occupancy estimates with a person.

In one example of estimating mobile device location using a locationmodel 412; a mobile wireless device 130 (e.g. a smartphone) can measurethe signals strength (RSSI) of first signals 140 transmitted by aBluetooth enabled refrigerator. The smartphone can record and transmitthe RSSI values as mobile device location data in second signals 185.The central controller can have previously identified the location ofthe refrigerator as being in the kitchen of the building and identifyfirst signals 140 from the refrigerator, based on a unique identifier inthe Bluetooth signals. The central controller 150 can aggregate andstore the signal strength of first signals (refrigerator) as reported bythe smartphone 130 a to the central controller 150 and add thesehistorical values to location model 412. Over time the centralcontroller and location model 412 can identify the peak RSSI typicallyreported by a smartphone and estimate that the smartphone is located inthe kitchen (e.g. the known location of the Bluetooth signalstransmitter) during those times. In general the mobile device locationestimator 420 can compare aggregated RSSI values in second signals 185with historical values in a location model 412 in the process ofestimating the location of the smartphone. The model may also functionto improve predictive capability in situations where, mobile devices areeither not available or all mobile devices have low or zero proximity topeople. In this case, the first data from fixed wireless sensors and thelocation model 412 can provide a more accurate occupancy estimate. Forexample if one or more fixed wireless sensors estimate that a person isin the general region of the kitchen, their location can be furtherrefined by searching for previous instances with similar fixed wirelesssensor data patterns from the location model 412. In this casehigh-confidence previous locations can include situations where mobiledevice locations with high person proximity weightings were available.

The occupancy estimator 408 can contain a proximity estimator 424,operable to receive aggregated proximity indications from second signals185 or from mobile device data aggregator 405. In FIG. 4D the centralcontroller 150 is located inside a wireless mobile master device andproximity estimator 424 can receive proximity data from sensors 423 bydirect electrical signal transfer (e.g. I2C, SPI, UART) without the needfor second wireless signals. The proximity estimator 424 determines anestimate of the proximity between one or more people 152 and one or moremobile devices 130. The proximity estimator 424 can calculate anaggregate person proximity estimate for each mobile wireless device,based on aggregated sensor data across time. For example, the combinedacoustic measurements across the last 10 samples from a smartphone mayindicate that someone is typing on a keyboard close by. Data frommultiple sensors, multiple mobile devices and multiple times may becombined. The proximity estimator can prioritize recent samples, forexample a device that has recently been put on a flat surface mayindicate a diminishing correlation between the device location and theperson location and therefore a rapid decrease in proximity. In someembodiments first data from fixed wireless sensors can be used toenhance person-to-device proximity estimates. For example, a person canput a smartphone on a flat surface, indicating that direct proximity hasceased. The process of estimating the proximity of the person to thesmartphone in the following minutes can be based in part on fixed sensor(e.g. motion) data indicating movement of the person. If fixed sensordata indicates no occupancy change (e.g. a person leaving the room) theproximity estimate can slowly decay as the smartphone sits unattended.Alternatively, motion indications from fixed wireless sensors concurrentwith second signals 185 reporting horizontal placement of the smartphonecan cause the proximity estimate to rapidly decrease. Some of theproximity data in second signals 185 may be a flag operable to indicatedirect proximity of a person, thereby reducing or eliminating theprocessing required by the proximity estimator 424.

Proximity estimator 424 can include a digital signals processor (DSP)capable of identifying sounds associated with person proximity (e.g.typing, snoring, conversing) and sounds that are not necessarilyassociated with a person (e.g. a refrigerator motor, birds chirping). Insome embodiments the proximity estimator can compare proximityindications (e.g. sound and vibration data) with a database ofidentified patterns in order to estimate proximity. For example asmartphone can report sound data to a central controller 150 and theproximity estimator 424 can access a remote server (e.g. on the WorldWide Web) to compare sound samples with a database of audio tracks frompopular television channels. In this way the proximity estimator canidentify a person changing television channels, proximal to thesmartphone. In some embodiments the proximity based on pattern matchingcan be performed locally using a computer process (e.g. DSP). In otherembodiments proximity estimator 424 can submit proximity data for remoteidentification by a processor connected over a data network (e.g. WorldWide Web, Ethernet or LAN). The one or more proximity estimates fromestimator 424 can be in the form of a digital value within a range ofvalues (e.g. a scale of 1-10), can be unique to each mobile device andcan be aggregated across all mobile devices that occupy a common region(e.g. a kitchen). The proximity estimator can use knowledge of the brandand model of a mobile wireless device 120 to identify a transferfunction relating proximity data to a proximity estimate. For example,knowledge of the capabilities of a large number of mobile devices can bestored in a database and indicate the vibration sensitivity or dataformat associated with the accelerometer or other state sensor in aparticular model of mobile wireless device.

Central controller 150 can identify a proximity criterion for mobiledevices. Central controller 150 can assess a proximity criterionidentify mobile devices 130 that satisfy the proximity criterion. Mobiledevices that pass the proximity criterion can be used in the weighingprocess. In another embodiment, if the proximity criterion is satisfied,the central controller can add useful data to the location model 412.For example it is ill-advised to attempt to correlate all mobile devicelocations with fixed wireless sensor occupancy indications, since amobile device may be left unattended. However, if the proximitycriterion is satisfied, the central control can add ana aspect of therelationship between occupancy indication in first signals and theestimated mobile device location. Examples of proximity criterioninclude: mobile devices where second signals indicate a strong proximityflag, a score of 5 or more out of 10 or an indication of directproximity at any time in the last 30 seconds. The proximity criterioncan be assessed based on aggregated proximity data from 404 a.

In FIG. 4A occupancy estimator 408 contains a weight generator 430,operable to determine proximity-based weights for some or all of themobile device location estimates. In one aspect of several embodimentsweights can be based on estimates from the person proximity estimator424. A mobile device with a low person proximity indication couldreceive a low weighting. Device location estimate may receive highweighting if there is an indication of strong proximity between a personand a mobile wireless device. A weight can be a numeric value or afunction. For example, a weight for a particular mobile device locationis equal to a factor (e.g. 4) multiplied by a detected proximityestimate or various numeric values associated with qualitative proximitylevels. Examples of weights assigned to various proximity levels caninclude: proximal=1, not proximal=0; a person in the distantbackground=0.1; indirect proximity indicative of a person likely in thesame room=0.5; direct proximity with the mobile device with or withoutdirect user input=1.0. Weights can be determined based on recentproximity estimates. The weight generator can account for theplausibility of weighting based on recent data. For example, based onhistorical patterns it can the weight generator can determine a personchecks their smartphone periodically during advertising breaks in atelevision show at a certain time each day. This information can bestored in the location model 412 or similar historical pattern storage(e.g. historical user model 455). Weights for the smartphone data canremain high short periods of low direct proximity (e.g. flat placementon a coffee table) during the time associated with regular periodicproximity. Proximity can be estimated based on a database of proximitydata for similar devices (e.g. a similar model smartphone or tablet PC)accessed on a remote server. This approach could be particularly usefulfor identifying the antenna gain of particular mobile wireless devicesand thereby improving both proximity and mobile device locationestimates.

In FIG. 4B weight generator 430 combines aggregated first data from 404a and proximity estimates from 424 to generate weights for mobile devicelocations. In some embodiments aggregated fixed sensor occupancy datacan be used to normalize the mobile device location estimates. Forexample during periods with strong clear occupancy indication formbuilding based sensors (e.g. a person entering a room and walking past awireless motion sensor), weights for mobile device locations can beuniforms attenuated (e.g. divided by 2). Conversely during periods ofweak occupancy signals (e.g. during periods when a person is sleeping orsitting quietly) proximity weights can be uniformly amplifier. Thusproximity estimates can produce larger weights, relative to the weakeroccupancy signals from fixed wireless sensors 120. In some embodiments,weights can be generated for both mobile device location estimates andaggregated occupancy data in first signals, thereby allowing forhighlighting of the most relevant data. Weight generator 430 can useproximity estimates in conjunction with learned rules. The rules can bebased on a database of transfer functions indicating how particularproximity signals correlate to the range of a person for a particularbrands or models of mobile wireless devices. Weights can be based on thehighest proximity estimate for a mobile wireless device in a particularperiod of time.

Occupancy estimator 408 produces one or more occupancy estimated for oneor more regions of a building. In FIG. 4A the occupancy estimate 409 canbe based on occupancy data from the fixed sensor data aggregator 404 andproximity-weighted mobile device location data from weight generator430. Proximity estimator 408 can combine these two types of occupancyand location estimated in a variety of manners, including combinatorialor based on the highest overall indication of proximity in a region. Theoccupancy estimate 409 can be in the form of a probability of occupancyfor an array of locations (e.g. X, Y grid), for example 0.1 at (X=1,Y=1), 0.0 at (X=1, Y=2), 0.2 at (X=2, Y=1) and 0.7 at (X=2, Y=2). Theoccupancy estimate can be the region with the highest probability ofoccupancy. The occupancy estimate can be the total estimated number ofpeople in region, for example the occupancy estimate for a kitchen canbe 3. The occupancy estimate for a plurality of regions can be chosen toaccount for an estimated total number of occupants in the plurality ofregions. For example a one bedroom apartment with two occupants couldhave the following occupancy estimates: kitchen=0.5, bedroom=0.1,bathroom=0.0 and living room=1.4. The occupancy estimate can be binaryfor example a region can be “OCCUPIED” or “UNOCCUPIED” or in computerlogic terms OCCUPIED=1 and UNOCCUPIED=0. An occupancy estimate of 1 orOCCUPIED can be assigned to a region in which the probability oraggregated indications of occupancy is greater than a threshold (e.g.probability greater than 50%). By combining occupancy indications frommultiple fixed wireless devices together with proximity-weighted mobiledevice locations the occupancy estimator can significantly narrow thesize of a particular region in which occupancy has a high probability.For example a motion sensor may report that the general area of thekitchen is occupied, while a Bluetooth enabled refrigerator may be ableto report that the refrigerator door has just opened and hence theregion of occupancy is in front of the refrigerator. A smartphone in theperson's pocket can further report a strong proximity to the person andthe central controller can further narrow the region of occupancy.

The occupancy estimator 408 can estimate the number of people in aregion based on the a combination of fixed and mobile device dataindicating multiple occupied regions of the building or occupancy thattransitions form one region to a region that is already occupied. In oneembodiment of FIG. 4B the occupancy estimate is based on based ongenerating proximity-based weights for first data indicating occupancyby fixed sensors and mobile device locations. In another embodiment ofFIG. 4B the proximity estimates from 404 b and aggregated fixed sensoroccupancy data from 404 a are used to weigh only the mobile devicelocation estimates from location estimator 420.

The central controller 150 can contain an occupant identity estimator416, operable to generate one or more identity estimates for a personresponsible for the occupancy estimate in one or more regions of thebuilding. The identity estimator 416 can provide identity estimates tothe third device selector 434 and can provide identity estimates to ahistorical profile weigher 470. The identity estimate can be based onpast sensor readings indicating locations commonly occupied by aparticular person, activity on a wireless device (e.g. smartphone,tablet PC) associated with a particular person or voice recognition. Theidentity estimator 416 can receive weights, mobile device locationestimates and fixed sensor occupancy data (first data) and estimate theidentity of a person associated with some or all of the data. In someembodiments identity estimator 416 can associated some of the aggregatedfixed sensor data with an identity based on coincident mobile devicelocations. In some embodiments an identity estimate 416 can be used toselect media content, data or device settings for automation devices110, based on a user's preferences. In one embodiment the identityestimate can be based on determining that aggregated first data fromfixed wireless sensors has strong correlation to data in a historicaluser profile (e.g. favorite locations 458, and patterns of occupancy459).

In other embodiments, identity estimator 416 can generate multipleestimates for the identity of a person corresponding to an occupancyestimate in a region. The one or more identity estimates can be aprobability associated with each candidate identity. Identity estimator416 can be a processor operating with an adaptive algorithm, operatingon a variety of data streams including first data, mobile devicelocation data, proximity data and occupancy estimates. Identityestimator 416 can be a neural network processor whereby multiple inputs(e.g. time of day, first data, and historical user profile data) arecombined with adaptive weightings to produce one or more identityestimates associated with an occupancy estimate 409. For example,aggregated first data from fixed wireless sensors 120 can indicateoccupancy in a home office that is historically only occupied by personA. In response to this occupancy estimate, identity estimator 416 cangenerate a high identity estimate for identity A (e.g. probability=90%for identity A or A=0.9). In some cases the identity estimator 416 canreceive a signal indicating that devices associated with a particularidentity are active elsewhere (e.g. in another building), and cangenerate a low (e.g. 0.1 or 0) probability estimate corresponding to aparticular identity. In some cases identity estimator 416 can useprevious data that is highly indicative of a particular identity toindicate present identity of the occupant (e.g. if there is only oneoccupant in a building and that occupant has been associated with recenthigh identity estimates for person A then subsequent occupancy estimatescan inherit the high identity estimate for person A, based on the singleoccupant fact. Interaction with personal wireless devices (e.g. asmartphone) can provide a persistent indication of identity to theidentity estimator 416.

Examples of identity estimates are: 90% probability of being person A,the person identified as the primary user of a mobile wireless device oran identity probability for each of the members of a family. For examplea house can have three occupants, identified within the centralcontroller as “FATHER”, “MOTHER” and “CHILD”. Example identityestimates, in response to occupancy indications are {0.1 FATHER, 0.8MOTHER, and 0.1 CHILD}. In another example the central controller canreport an occupancy estimate of 2 in a region (e.g. kitchen), indicatingtwo people in the region. Identity estimates can be chosen such that sumof identity estimates is equal to the occupancy estimate (e.g. 0.9FATHER, 0.9 MOTHER and 0.2 CHILD).

The central controller 150 contains a third device selector 434,operable to select one or more third devices 110, based in part on oneor more occupancy estimates 409 for one or more regions of the building.Third device selector 434 can select one or more electronic devices 110based on factors such as the geographic location relative to the regionwith highest proximity-weighted occupancy estimate 409. The thirddevices 110 can also be selected to provide a particular response at theestimated region of occupancy (e.g. a constant light level at the userlocation as they move throughout a building or a constant sound volumeat the estimate room occupied). The third device selector 434 may use amodel to anticipate the effect on a user of operating one or more thirddevices 110, based on the estimated occupied region. For example awireless speaker may receive a signal to decrease volume if an occupancyestimate indicates a person in a region directly in front of a speaker.In another example, third device selector 434 can select a light at theend of a hallway as a third device and can signal the light toilluminate at a higher intensity if the person is far away (e.g. greaterthan 10 ft) to provide sufficient light intensity at a region associatedwith a high occupancy estimate 409.

Central controller 150 can contain a third signal generator 442 operableto generate third signals 190. Third signals can be based on theoccupancy estimate for one or more regions of the building. Thirdsignals 190 can include commands addressed to one or more third devices110. In some embodiments third data identified third devices selected bythe third device selector 434. In this way, many devices can receive thethird signals and third data can identify if the data is intended foruse by a particular device. Third data can include commands or settingchanges (e.g. turn on, turn off, change volume or light intensity) andthird data can be based on part on the estimated occupancy of one ormore regions.

The third signal generator 442 to generate one or more third signals 190based on the occupancy estimate for one or more regions of the buildingin a wide variety of manners. For example the third signals generatorcan address third signals to all third devices in a region thatindicates a particular occupancy estimate (e.g. turn on all lights in aregion that exhibits an occupancy estimate greater than 0.9). In anotherexample a third signals generator could generate a third signaladdressed to a parent's mobile phone when an occupancy estimate in inaccordance with this disclosure is generated in the hallway of a house,indicating that children have arrived home. The third signal generator442 can choose between several reformed third signals using an occupancyestimate as the basis. The third signal generator can insert data intothe third signal using the occupancy estimate as basis; examples includea particular device ID, wireless session ID, neighboring region, firstdata, second data, occupancy estimate or identity estimate. For examplein response to an occupancy estimate in a region the third signalgenerator can include in third signals or third data, one or moreidentity estimates corresponding to the occupancy estimate. In anotherexample third signal generator 442 makes choice regarding the content ofthird signals based on an occupancy estimate and that content selectioncan persist for some time afterwards. For example, in response to anoccupancy signal in a region, third signals generator can select one ormore wireless speakers, select or negotiate a session ID with thewireless speakers and continue to transmit third signals to the wirelessspeakers for hours or days afterwards. In other examples aspects ofthird signals are generated in part or indirectly as a result ofoccupancy estimates in a region of the building. The disclosed systemenables a wide variety of conditions, computer logic or“if-this-then-that” IFTTT automation software, to determine how thirdsignals 190 are based on occupancy estimates for regions of thebuilding.

Third signal generator 442 can combine commands with media from a mediastorage device 446 to produce wireless signals. Examples of mediastorage include flash memory, CD-ROM, hard-disk media and RAM. Commandscan instruct devices to perform one or more specific actions on a mediasignal, for example to attenuate, filter or delay a music stream. Inthis way the central controller 150 can output a broadcast streamingmedia signal (wireless audio, TV or video) with specific instructionsfor electronic devices to tailor the media signal for a desired effectin an occupied region. For example several speakers could receive abroadcast digital music stream (e.g. MP3, WMA or PCM) and each speakercould receive a command including a volume and delay (e.g. 20milliseconds) such that sound from several speakers would reach a user'sestimated location simultaneously. Signals from the signal generator 442can be transmitted by the transmitter 402.

Central controller 150 can contain one or more transmitters 448 operableto send one or more third signals 190 to one or more third devices 110.Transmitter 448 can be a wireless transmitter (e.g. WiFi, 4G, Bluetooth,Zigbee, Z-wave) or transmitter 448 can transmit third signals 190 over awired interface to automation devices. Examples of wired transmissionprotocols include Ethernet, Ethernet over powerline, Homeplug AV, USB.Transmitter 448 can use a variety of wired and wireless protocols todistribute audio or video streams to speakers and displays locatedthroughout the building. Transmitter 448 can be indirectly coupled tothird devices 110 using a data transmission network. For example, thirdsignal generator 442 can generate web traffic such as TransmissionControl Protocol/Internet Protocol (TCP/IP) or User Datagram Protocol(UDP) packets intended for a third device 110 and transmit these packetsto a server on the world wide web. The server can be operably coupled tothe third device and can subsequently transmit the packet to the thirddevice. This type of server-relayed third device signaling hasadvantages in terms of signal reliability and storing copies of thirdsignals.

Historical User Profile

FIG. 4C illustrates an embodiment in which a central controller 150 usesa plurality of historical user profiles 455 in the process transmittingsignals to third devices 110. A historical user profile 455 can begenerated for one or more individuals who frequently or repeatedlyoccupy the building and account for some of the occupancy estimates. Forthe purposes of this disclosure the term “profile owner” is theindividual associated with a historical user profile 455. A historicaluser profile can contain data regarding a profile owner's identity (e.g.a screen name, user ID a nickname or a unique identifier relative toother historical profiles or an assigned name in the centralcontroller). A historical user profile 455 can contain data regardingprevious locations and occupancy patterns within the building, common orfavorite occupied regions, location patterns and daily routines (e.g.sequences of regions occupied) and average dwell times. Historical userprofile 455 can include one or more profile identifiers 456. Identifiers456 can be identifying descriptors for the profile owner (e.g. a name ora unique number corresponding to a person, a screen name, user ID,nickname or can be the next unused number in a sequence of availableprofile numbers at the time the profile is created.)

Historical user profile 455 can include data regarding a list ofelectronic devices 457 frequently used or associated with the profileowner. The list of associated devices 457 can be a hash table ofelectronic devices known to the central controller 150 and an indication(e.g. a binary “1”) of the electronic devices in the hash tableassociated with the profile owner (e.g. a profile owner can beidentified as the primary user of a dishwasher, car, tablet PC andsmartphone #3 from within the known devices.) Historical user profile455 can include a list of favorite location 458. Favorite locations 458can be regions of the building frequently occupied by the profile owner.Favorite locations can be based on dwell times and frequency of visitingthe same region or generating strong occupancy signals at the same fixedwireless sensors 120. Favorite locations can include an office, a homeoffice, a preferred bathroom or bedroom. An advantage of the presentdisclosure is that separating favorite locations based on profileowners, can provide improved indication of individual user habits andcorrelation between regions of the building. This is an improvement overprevious methods that averaged occupancy across all users. Favoritelocations can include sequences of locations and routines based on thetime of day.

Historical user profile 455 can include data regarding one or moreroutes 459 traveled by the profile owner. Routes 459 can include a listof regions and may contain the sequence in which regions are visited.Routes 459 can have an associated time of day or day of week (e.g.profile #1 with identity=Dad can contain morning sequence={Bedroom,Bathroom, Bedroom, Kitchen, Garage} and evening sequence={Garage,Kitchen, Home Office}.) Favorite locations 458 and routes 459 can havedwell times associated with them (e.g. average time spent in a room).Low dwell times for a region may indicate that the region is a waypointbetween two rooms (e.g. a hallway).

Historical user profile 455 can include a location correlation matrix460, indicating the probability that occupancy indications in a firstregion will be followed by occupancy in a second region at some timelater. For example a motion sensor placed in a hallway can produce highoccupancy estimates that can be time correlated with high occupancyestimates in rooms connected to the hallway a short time later. Twoexamples of location correlation matrices are illustrated as 910 a and910 b in FIG. 9. Each element in the location correlation matrices 910 aand 910 b indicate the probability that occupancy in a first region ofthe building will be followed by occupancy indications in a secondregion some time later. Elements of a location correlation matrix 460can be normalized, for example normalized to 10 (e.g. the first element930 a of location correlation matrix 910 a is equal to 7 indicating thatthere is a 7/10 probability that region 1 will sense a person some timeafter initially sensing a person at sensor 1. In many cases the mostlikely future occupied region is the current occupied region. Similarly,location correlation matrix 910 a indicates there is a 3/10 probabilitythat a person sensed in region 1 will be sensed in region 2 at some timelater. The second location correlation matrix 910 b indicates there is a2/10 probability that a high occupancy estimate in region 1 will befollowed by a high occupancy indication in region 2 some time later.

Elements of the correlation matrices 910 can be stored as a series ofprobabilities corresponding to different times after some start time.For example, the start time can be indicated by detecting an aspect of aperson at region #1. The memory required to store the locationcorrelation matrix can be reduced by choosing an appropriatemathematical function (e.g. Gaussian probability distribution) todescribe the time-dependent correlation between two regions. Forexample, the location correlation between activity in region #1 (e.g.kitchen) and a region #2 (e.g. bedroom) can be stored by storing thepeak correlation (e.g. 0.3 or 30%) and the corresponding peakcorrelation time (e.g. the average time for a person to walk from thekitchen to the bedroom). It can be appreciated that the time correlationbetween different locations measured in this manner could vary greatlyfrom person to person depending on their speed of walking and preferredroutes. One method to capture the location correlation matrix in acompact manner is to store the peak proximity and the time associatedwith the peak probability (e.g. the maximum correlation=0.3 at t=5seconds). A historical user profile 455 can further store one of anumber of standard probability distributions that best fits the locationcorrelation matrix corresponding to the historical distribution ofarrival times for profile owner. It can be appreciated by those skilledin the art that shoring a location correlation matrix as a parameterizedfunction (e.g. Gaussian distribution with peak probability of 0.3 andmean transition time of 5 seconds) is more efficient in comparison tostoring the measured time-series correlation function between tworegions of the building.

Data in a historical user profile 455 can be updated from a variety ofdata sources including, first data and second data, when the identity ofan occupant can be determined with sufficient accuracy. Historical userprofiles 455 can be stored in the central controller. It will also beapparent to a person of skill in the art that historical user profiles455 can be stored remotely from the central controller 150 and accessedover a data network (e.g. World Wide Web, Ethernet, local area network).

Central controller 150 can generate and apply an identity criterion toidentity estimates generated at 416. The central controller can begin bygenerating an occupancy estimate for a region of the building and anassociated identity estimate for a person occupying the region. Centralcontroller 150 can apply the identity criterion to the identityestimate. An example of an identity criterion is a confidence level(e.g. 75%) that the identity is correctly estimated. For example, in abuilding with four people the central controller may apply an identitycriterion of 75% confidence level before initiating a person-specificautomation response. In one embodiment, if the identity criterion issatisfied, the central controller 150 can modify one or more aspects ofthe corresponding historical user profile (e.g. favorite locations,routes or associated mobile wireless devices). This aspect providesmeans to ensure the accuracy and specificity of each historical userprofile 455 to the profile owner.

Central controller 150 can include a profile weigher 470. Profileweigher 470 can receive one or more identity estimates from 416 andaspects (e.g. routes) from historical user profiles 455 corresponding tothe identity estimates. Profile weigher 470 can weigh aspects of one ormore profiles based on the identity estimate and can send identityweighted profile indications to third device selector 434.

In one embodiment of FIG. 4C the occupancy estimator can have asimplified structure including a fixed sensor data aggregator 404 a, amobile device location estimator 420 and an occupancy estimator 409.This embodiment does not need to have a proximity estimator and can relyon historical user profiles 455 and profile weighting at 470 to enhancethird device selection at 434.

Description Additional Embodiments

FIG. 4D illustrates an embodiment in which the central controller 150 islocated inside a mobile wireless master device 155. In this embodimentthe location estimator 420 can receive wireless master location data421. Wireless master location data 421 is calculated similar to othermobile devices 130. The estimated location can be based on an analysisof one or more localization signals 165. Analysis of the signals canallow for an estimate as to which of external devices are relativelynear the wireless master device 155, which can allow for an estimationof a location of the wireless master device 155. For example, theanalysis can identify one or more Bluetooth beacons, fixed wirelesssensors 120, automation devices 110, cell towers 170, GPS satellites 175or WiFi access points 180. Devices transmitting localization signals 165can be associated with a known location, such that a location of thewireless master device 155 can be estimated, e.g., via a triangulationor trilateration technique. In embodiments where central controller 150is located inside a wireless master device 155 there is no need towirelessly transmit wireless master location data 421 and instead thedata can be transmitted by wired means (e.g. I2C, SPI, UART) to themobile device location estimator 420. In the embodiment of FIG. 4Dproximity estimator 424 can receive proximity data directly fromproximity sensors 423 within the wireless master device. For example atablet PC with a central controller can receive indications of personproximity from an onboard accelerometer 423 a, microphone 423 b, lightsensor 423 c, temperature sensor 423 d, gyroscope 423 e, signal strengthindicator 423 f, or magnetometer 423 g.

In some embodiments one or more elements of the central controller canbe remotely located (e.g. located in a server bank) relative to theother elements. Examples of elements that could be remotely locatedinclude data aggregators 404, proximity estimator 424, mobile devicelocation estimator 420, weight generator 430, location model 412,identity estimator 416, media storage 446 and third device selector 434.Remote located elements can be operably connected to the building basedelements of the central controller via a communication network (e.g.World Wide Web, Ethernet, Local Area network LAN). In some embodimentsthe majority of the elements comprising the central controller can belocated outside of the building containing the plurality of fixedwireless sensors and mobile wireless devices. For example only the oneor more wireless receivers 402, transmitter 448 and third signalsgenerator 442 need be located in or close to the building 115. In thisway the central controller can be embodied as a plurality of costeffective receivers, transmitters and signal generators in or close to ahome and more costly elements can be stored in a secure remote locationallowing for maintenance and upgrades. While advances in remotecomputing can enable several elements of the central controller to beremotely housed from the indoor transceivers the central functionalityof augmenting fixed building based sensor indications withproximity-weighted mobile device location indications remains largelyunchanged.

In another aspect there has been significant progress towardsintegrating multiple integrated circuited into multichip modules. Oneexample is the Intel Edison module available from Intel of Santa ClaraCalif. In one embodiment of this disclosure many of the centralcontroller elements can be disposed on a multichip module. Receivers 402can be embodied as a combination Bluetooth/WiFi transceiver, aggregators404 a and 404 b can be embodied in cache RAM or FLASH memory. The mobiledevice location estimator 420 can be software running on a processorwith an arithmetic logic unit to calculate estimated device locations,the weight generator and proximity estimators can be fixed algorithms ormachine learning algorithms running on a processor or a neural networktree within a microchip. Historical user profiles 455 and locationmodels 412 can be stored in local memories or stored on a remote server.

Operation

In one embodiment the operation of a central controller to transmitsignals to one or more third devices can include the following steps.Fixed wireless sensors transmit first signals to the central controllerupon sensing occupancy within an indoor space. One or more mobiledevices can calculate location data indicative of the device geographicposition and proximity data indicative of the mobile device proximity toa person. The mobile devices can transmit second signals, containinglocation and proximity data. The central controller receives first andsecond signals, calculates a person proximity indication for each mobiledevice and weights mobile device location estimates based on theproximity indication. The central controller can estimate the occupancyof one or more regions of the building based on the occupancyindications in first signals and the proximity-weighted devicelocations. The central controller 150 can select one or more buildingautomation devices 110, based in part on the proximity-weighted locationestimates. Mobile devices 130 a that are proximal to people 152 canindicate their proximity and be weighted more heavily in the process ofselecting one or more third devices. Similarly, mobile devices 130 bthat have been left unattended (e.g., on a desk 160) can indicate theirlack of proximity, and receive lower weighting and relevance when thecentral controller select third devices 110.

FIG. 5 is a flow diagram for a process 500 of generating first signals140 at a fixed wireless sensor 120, in accordance with one embodiment ofthis disclosure. At block 510 one or more analog sensors 200, buttons205 a or actuators 205 b can detect an aspect of a person. At block 520sensor samples can be processed to form first data. Processing to formfirst data can be performed by a processor 240, circuitry or software.At block 520 the determination of sufficiently indicative sensor datacan be based in part on adaptive algorithms executed by the processor240. The adaptive algorithms can increase threshold values to reducefalse positives caused by sources non-human sources e.g. pets, swayingtrees, changes in room temperature and HVAC systems operation. Examplesof first data include raw sensor values, maximum sensor values in a timeperiod, average sensor values, combinations of sensor values operable toindicate occupancy. For example some motion sensors use passive infraredand ultrasound in combination to estimate occupancy. Fixed wirelesssensor 120 can have a field of view and first data can be occupancyindications in a region that comprises that field of view. First datacan be indicative that the region sensed by a fixed wireless sensor isunoccupied. In some embodiments an analog circuit can replace thefunction of the CPU and generate first data based on an analogtransformation of sensor, button or actuator values. For example ananalog motion detector can have a PIR sensor 200 e, and analog amplifierwherein motion values above a particular threshold can cause a theanalog amplifier to generate a first signals with an indication ofoccupancy in the field of sensing of the wireless sensor.

At block 530 one or more signals can be received, instructing the fixedwireless sensor to generate first signals in the absence of the activitycondition being met. This can happen in response to the centralcontroller 150 or other fixed wireless sensors 110 detecting sufficientactivity and requesting fixed wireless sensors to report data forcomparison and for location purposes.

At block 540 one or more signals can be generated (e.g. by the signalgenerator block 230). The signals can include a unique identifier of thefixed wireless sensor and raw or processed sensor data. The signal canbe generated in accordance with the communication protocol 220. At block550 the first signal can be transmitted.

FIG. 6 is a flow diagram for a process 600 of generating second signals185 at a mobile device 130. At block 610 the mobile wireless device 130can receive wireless localization signal 165. The wireless localizationsignals 165 can provide geo-location data (e.g. a GPS satellite 175) orcan be signals from for example a cell-tower 170, WiFi router 180,Bluetooth beacon, home automation device 110 or fixed wireless sensor120. At block 620 wireless localization signals 165 can be processed bythe mobile device location estimator 315 to generate an indication ofmobile device location. The location estimate can be a distance estimatefrom one or more transmitters or can be a measure of the relative signalstrength from several transmitters. At block 630 sensor data is gatheredfrom mobile sensors indicative of device state (e.g. power state, angle,direction relative to magnetic north) and sensor data indicative ofclose proximity to a person (e.g. acceleration associated with beinghandheld, placement in a pocket purse, angle or vibration indicative ofbeing handheld). At block 630 data indicative of direct user interactionsuch as typing or pressing a touchscreen can be gathered.

At block 640 the data from block 630 is processed to identify anindication of close proximity to a person (e.g. monitoring accelerationover time). The processing can occur in the data processor 320 and caninvolve various degrees of processing including filtering, maximum andminimum value detection, pattern recognition and principal componentanalysis.

At block 650 the processed data from block 640 is evaluated to determineif the mobile wireless device has direct proximity to a person (e.g.direct user input or sensor data indicative of direct proximity). If thedirect proximity condition is not met additional sensor data is gatheredat block 660, indicative of long range or indirect proximity of a person(e.g. acoustic indication of a person close by, vibration indication ofa person close by or changes in signal strength indicative of a personclose by). In some embodiments, if the close proximity condition is metat block 650 the mobile device can skip block 660.

At block 670 third data indicative of direct and indirect personproximity is gathered. Third data can also include device state sensordata (e.g. tilt angle relative to the ground, direction relative tomagnetic north). Blocks 630-670 can be executed by the data processor320.

In some embodiments, blocks 630-670 can be executed in a repeated loopin order to maintain an up-to-data indication of person proximity. Thismay be beneficial in situations where there is rapidly changing orconstantly varying proximity.

In some embodiments, the mobile device can maintain an up-to-date flagor variable indicating direct person proximity, indirect proximity or noproximity. The flag or variable can be generated using some of blocks630-670. The proximity flag can be used to indicate direct or indirectperson proximity and one some or all of blocks 630-670 can be skipped.

At block 680 second signals are generated including location andproximity data, in accordance with the communication protocol 340. Atblock 690 one or more second signals are transmitted by the wirelesstransmitter 350 to the central controller 130.

FIG. 7 is a flow diagram of a process for transmitting third signals toone or more third electronic devices according to aspects of the presenttechnology. At block 705 the central controller receives first signals140, containing sensor data indicative of occupancy of a region of thebuilding form fixed wireless sensors 120. At block 710 first data isaggregated to gather sufficient samples and to highlight importantevents (e.g. the maximum sensor reading for a sensor among the last 10readings). At block 720 central controller 150 receives second signals185 from a mobile device, where second signals contain data indicativeof mobile device location and data indicative of proximity between amobile device and one or more people. At block 725 central controller150 estimates the location of the mobile device. At block 730 centralcontroller 150 estimates the proximity of a person to the mobile device.At block 735 central controller 150 calculates weights for the devicelocation estimate from 720, whereby the weights are chosen based in parton the proximity value calculated at 730. In some embodiments, theweights for both the mobile device location estimate and aggregatedfirst data are calculated at block 730. In yet other embodiments, theweights for the mobile device location are calculated based on theperson proximity estimate and the based on the aggregated first data. Atblock 740 an occupancy estimate is generated for one or more regions ofthe building, based on the weights and first data.

At block 745 central controller 150 can select one or more of thirddevices 110, whereby the third devices are selected based in part on theoccupancy estimate. In another embodiment at block 750 centralcontroller 150 generates third signals, wherein at least some of thedata in third signals is based on the occupancy estimate for one or moreregions of the building. At block 755 central controller 150 can combinecommands with media content or other data to be sent to at least some ofthe subset selected at block 745 to generate third wireless signals. Atblock 760 central controller 150 transmits third wireless signals to oneor more third devices.

FIG. 8 is a flow diagram of the operation of a central controller thatis housed inside a mobile wireless device and is operable to send thirdsignals 190 to one or more third devices 110 according to an aspect ofthe technology. At block 805 the central controller receives firstsignals, containing sensor data indicative of occupancy of a region ofthe building form fixed wireless sensors 120. At block 810 first data isaggregated to gather sufficient samples and to highlight importantevents (e.g. the maximum sensor reading for a sensor among the last 10readings). At block 820 central controller 150 receives second signalsfrom a mobile device, where second signals contain data indicative ofmobile device location and data indicative of proximity between a mobiledevice and one or more people. At block 825 central controller 150estimates the location of the mobile device. At block 830 controller 150gathers data from proximity sensors 423 associated with the wirelessmaster device 155 and central controller estimates the proximity of aperson to one or more mobile wireless devices 130. At block 835 centralcontroller 150 calculates weights for the device location estimate from820, whereby the weights are chosen in part based on the proximity valuecalculated at 830. In some embodiments the weights for both the mobiledevice location estimate and aggregated first data are calculated atblock 830. In yet other embodiments the weights for the mobile devicelocation are calculated based on the person proximity estimate and thebased on the aggregated first data. At block 840 an occupancy estimateis generated for one or more regions of the building, based on theweights and first data.

At block 845 central controller 150 can select one or more of thirddevices 110, whereby the third devices are selected based in part on theoccupancy estimate. In another embodiment at block 850 centralcontroller 150 generates third signals, wherein at least some of thedata in third signals is based on the occupancy estimate for one or moreregions of the building. At block 855 central controller 150 can combinecommands with media content or other data to be sent to at least some ofthe subset selected at block 845 to generate third wireless signals. Atblock 860 central controller 150 transmits third wireless signals to oneor more third devices.

FIG. 9 is a functional diagram of the operation of the historicalprofile weigher 470. FIG. 9 shows two location correlation matrices 910a and 910 b correspond to two people, who may be responsible foroccupancy indications. Each correlation matrix 910 a and 910 b relatespresent and future occupancy in five regions of the building. Locationcorrelation matrices 910 a and 910 b are weighed by profile weigher 470in accordance with two identity estimates from 416. In this embodimentthe profile weigher generates weighted location correlation matricesthat can be combined by a summation circuit 940. Central controller canselect third devices based on the combined result.

In the embodiment of FIG. 9 identity estimator 416 transmits a pluralityof identity estimates to profile weigher 470. For example identityestimator can transmit three identity estimates corresponding to threeidentities established in the central controller: 0.6 for FATHER 0.3 forMOTHER and 0.1 for CHILD. Profile weigher 470 can generate profile basedweights 920 a and 920 b based on two of the identity estimates. In someembodiments the profile weigher can use identity estimates as profileweights, in other embodiments the profile weigher can perform variousdegrees of process on identity estimates to form profile weights. Forexample low identity estimates can be neglected, only the highestidentity estimate can be converted into a profile weight or the first Nhighest identity estimates can be converted (e.g. N=2). In the exampleof FIG. 9 the two highest identity estimates are converted to profileweights (0.6 for FATHER and 0.3 for MOTHER). Profile weigher 470 canaccess the historical user profiles 455 associated with the profileweights and can select one or more common aspects from each profile. Inthe embodiment of FIG. 9 two location correlation matrices are chosecorresponding to the two highest identity estimates. Each correlationmatrix 910 can be weighted by the corresponding profile weighting 920 aand 920 b (e.g. each element of each matrix can be multiplied by theprofile weighting). The weighted correlation matrices can be combined acombiner 940. The combiner can be software or a circuit operable tocombine a plurality of weighted aspects of historical profiles. Exampleof operations performed by the combiner can include summation oridentification of a highly weighted region of the building. The combiner940 can produce an occupancy estimate for one or more regions of thebuilding. In one aspect the combiner can generate occupancy estimatesfor a time in the near future (e.g. 1-10 seconds later or severalminutes later). In this way the profile weigher 470 can generate anestimate of future occupancy, based in part on knowledge of theoccupant's identity and provide this future occupancy estimate to thethird device selector 434.

FIG. 10 is a flow diagram of the process for transmitting third signals190 to one or more third electronic devices 110 according to aspects ofthe present technology. At block 1005 the central controller canreceives first signals 140, containing sensor data indicative of aperson form fixed wireless nodes 120. At block 1005 central controller150 can also receive second signals from one or more mobile wirelessdevices 130. At block 1010 central controller 150 aggregates first datato gather sufficient samples and to highlight important events (e.g. themaximum sensor reading for a sensor among the last 10 readings). Atblock 1015 central controller 150 can generate an occupancy estimate forone or more regions of the building. A variety of other data sources canbe used at block 1015 to enhance the location estimate, including, thelocation model and one or more historical user profiles. At block 1017central controller 150 generates a plurality of identity estimatesassociates with first data or occupancy estimate for one or more regionsor first data indicative of occupancy. For examples a building can havefive workers, and the central controller can estimate the probabilitythat each of the workers is responsible for the occupancy estimate. Theidentity estimator 416 can use a variety of techniques at block 1017 topredict the identity of a person including, location patternrecognition, audio analysis, historical correlation, or recent strongproximity with mobile wireless devices associated with a primary user.For example a motion sensor associated with a building may not be ableto independently associate first data with a particular family member.However if strong motion signals in first data are occasionallyconcurrent with strong proximity-weighted smartphone locationindications from a smartphone associated with a first family member andrarely or never concurrent with the presence of other family membersmobile devices, then the identity estimator 416 can, generate multipleidentity estimates and generate a high probability estimate for thefirst family member. Estimates of identity may be binary correspondingto possible and impossible (e.g. it is possible that the occupant hasidentity A, B, or D but not C, E or F)

At block 1020 central controller 150 accesses one or more historicaluser profiles 455, containing historical user-specific occupancy datafor example occupancy patterns, favorite locations 458, commondestinations, common routes 459 (e.g. frequently going to the kitchenfrom the living room). Historical user profiles 455 can include alocation correlation matrix 450. In some embodiments central controller150 can further access a location model 412 for the indoor space.

At block 1025 weights can be calculated for one or more aspects of oneor more historical user profiles, whereby weighting are based in part onthe identity estimates generated at 1017. At block 1030 centralcontroller 150 can select one or more of third devices 110, whereby thethird devices are selected based in part on one or moreidentity-weighted historical user profile aspects. At block 1045 centralcontroller 150 can generate commands for the selected third devices. Inanother embodiment at block 1050 central controller 150 generates thirdsignals, wherein at least some of the data in third signals can be basedon one or more identity-weighted historical profile aspects. At block1050 central controller 150 can combine commands with media content orother data to be sent to at least some of the third devices selected atblock 1030 to generate third wireless signals 190. At block 1055 centralcontroller 150 transmits third signals 190 to one or more third devices110.

The following are example of the operation of several embodiment of thepresent disclosure. In one example, a person is located in a house withseveral fixed motion sensors and mobile wireless devices and anautomated lighting system. The person is types at a computer and isproducing weak occupancy indications measured by the motion sensors. Asmartphone associated with the person is on the desk nearby and reportsthe sound of typing as proximity indications to the central controller.The central controller processes occupancy sensor data from thesmartphone and several other mobile devices (e.g. Tablet PC elsewhere inthe home). The central controller determines a higher weighting for thesmartphone location, relative to the weak fixed motion sensor readings,based in part on an indirect proximity estimate provided by the typingsound data. In some cases the central controller, can commands the fixedmotion sensors to transmit first signals, thereby enabling thesmartphone to better estimate a location. The central controller canestimate the location of the person based on the smartphone and commandthe subset of automated lights in the room to remain on, even in theabsence strong motion sensor signals.

In another example with the same setup the person gets up from theirdesk in the home office to go to the kitchen, the fixed wireless motionsensors detect the motion and issues first signals. The smartphone canreport proximity data to the central controller indicating that it isstationary on the desk in the home-office (e.g. zero degree elevation),coincident with the motion indications, and therefore the centralcontroller can combine these pieces of information and determine thatthe smartphone no longer has close proximity to the person. In thiscase, the phone is not moving with the person and the central controllerweights the smartphone location data lower in the overall personlocation prediction. The advantage of the disclosed system is thatsmartphone location data can be rapidly deprioritized in favor of thefixed wireless senor data, thereby providing more accurate occupancyindication of various regions throughout the scenario.

In another example with the same setup the person gets up from a desk inthe home-office to go to the kitchen and carries their smartphone intheir pocket. The fixed motion sensors detect the motion and transmitfirst signals to the central controller. The mobile wireless devicesreport their locations to the central controller, in some cases usingfirst signals as localization signals sources. In this case, thesmartphone indicates strong proximity to the primary user, based on theacceleration and orientation, coincident with the motion signals. Thesmartphone issues a strong proximity flag as part of a second signal tothe central controller. The central controller applied a large weight tothe smartphone location data based on the strong proximity flag andestimates the occupancy of several regions in accordance with the motionsignals and smartphone location and commands the automated lighting toturn on in a predictive manner as the person walks to the kitchen.

In another example with the same setup the person is walking with theirsmartphone in their pocket. The proximity data in second signalsindicates the orientation of the smartphone antenna and placement of theperson relative to the smartphone. The central controller uses theproximity data (angle and placement and compass heading) to calculate acorrection factor for the mobile device location estimate to account forshadowing effect of the person's body, thereby improving person locationaccuracy.

In another example, a home is equipped with a system of integratedwireless speakers. Two people are present in the home, the first personis studying in a home office, the second is watching television whileand periodically interacting with a tablet PC. The television watcherputs the tablet PC down on a coffee table and moves towards the kitchen.The tablet PC reports an abrupt change in person proximity (e.g.transition from handheld to flat elevation). The central controllerreceives motion signals from fixed sensors near the tablet PC andestimates the identity of the person associated with the motion to bethe primary user of the tablet PC, based on the device-proximity change.The identity estimate can be further reinforced by a location modelaccessed by the central controller indicating that the tablet PCestimated location is historically correlated with the motion signalsfrom fixed sensors. Based on the motion signals, estimated occupancy andestimated identity, the central controller estimates the present andfuture location of the television watcher as they move to the kitchenand commands the wireless sound system to transfer the television audioto the kitchen. The system does not disturb the person studying in theoffice, based in part on their determined proximity to their smartphone.

In another example a central controller can receive first signals from aplurality of motion sensors indicating that a person is walking down ahallway in a building. The person can pick up a wireless car keyfob. Thecar key fob can report its location and an indication of proximity to aperson (e.g. based on recent motion) in second signals to the centralcontroller. The central controller can generate weights for severalmobile devices and weigh the keyfob location estimate more heavilyrelative to other mobile wireless devices in the process of estimatingthe occupancy of a region of the building. The central controllergenerate third signals that contain settings specific to the primaryuser of the keyfob (e.g. turn off the coffee maker or turn a televisionto a particular channel), based in part on the weights.

In another example a person enters a room, sits on a sofa andperiodically checks their cellular phone for new messages. If the personsits quietly, wireless motion sensors can report low indications ofoccupancy and previous automation systems may eventually assume theperson has left the room. The disclosed system enables recent estimatesof person-to-device proximity to cause a high occupancy weighting forthe cellular phone location. Similarly, the disclosed system providesmeans to reduce the cellular phone location weighting if the centralcontroller receives new first data indicates that the person is walkingwhile new proximity indications from the cellular phone indicates thatit is remaining stationary.

While the above description contains many specificities, these shouldnot be construed as limitations on the scope of any embodiment, but asexemplifications of various embodiments thereof. Many otherramifications and variations are possible within the teachings of thevarious embodiments. Thus the scope should be determined by the appendedclaims and their legal equivalents, and not by the examples given.

1. A device for transmitting signals, based on building occupancyinformation, the device comprising: one or more receivers wherein atleast one of the receivers receives first signals from one or more fixedwireless sensors associated with a building, each first signal being ashort range wireless signal, each first signal comprising first data,and first data being indicative of one or more people occupying one ormore regions of the building, and wherein at least one of the receiversreceives second signals from one or more mobile wireless devices,wherein second signals contain a proximity indication, indicative of theproximity of one or more people to one of the one or more mobilewireless devices and wherein the second signals contain a mobile devicelocation indication, indicative of the location of one of the one ormore mobile wireless devices; an occupancy estimator operable togenerate an occupancy estimate for one or more regions of the buildingcomprising: a data aggregator to aggregate at least some of the firstdata in the first signals; and a weight generator to generate one ormore weights for the mobile device location indications, wherein the oneor more weights are determined based at least in part on the proximityindication in second signals, and wherein the occupancy estimate for atleast one region of the building is based at least in part on aggregatedfirst data and the generated weights; a third signal generator togenerate one or more third signals based on the occupancy estimate forone or more regions of the building, each of the one or more thirdsignals comprising third data; and a transmitter that transmits the oneor more third signals to one or more third devices.
 2. The centralcontroller of claim 1, wherein second signals contain mobile devicelocation indications based on localization signals received by one ormore mobile wireless devices.
 3. The central controller of claim 1,wherein the central controller generates occupancy estimates for eachregion in a set of regions, associated with the building, wherein eachregion in the set is substantially non-overlapping and wherein the setof regions contains at least three regions.
 4. The central controller ofclaim 3, wherein each region in the set of regions covers an area lessthan half of the total area of the building.
 5. The central controllerof claim 4, wherein the occupancy estimator assigns each of the one ormore mobile wireless devices to a region from the set of regions,wherein the assignment is based at least in part on the mobile devicelocation indication in second signals and wherein the occupancy estimatefor the assigned region is based on proximity indications in the secondsignals.
 6. The central controller of claim 1, wherein the proximityindication in the second signals is from a set of signal qualityfeatures including received signal strength indication, multipathinterference, bit error rate, signal to noise ratio and time of flightdelay.
 7. The central controller of claim 1, further comprises: at leastone transmitter operable to transmits short range wireless advertisingsignals to one or more mobile wireless devices, upon satisfaction of acriterion, wherein the criterion is based on aggregated first data andwherein the short range wireless advertising signals contain a requestto transmit second signals.
 8. The central controller of claim 1,wherein the mobile device location indication is based on an aspect ofthe signal quality of second signals from a set of aspects including:received signal strength indication, multipath interference, bit errorrate, signal to noise ratio and time of flight delay.
 9. The centralcontroller of claim 1, further comprising an identity estimator operableto generate an identity estimate associated with an occupancy estimatein at least one region of the building and wherein third data is basedat least in part on the identity estimate.
 10. The central controller ofclaim 1, wherein third signals are transmitted to a plurality of thirddevices and wherein at least two of the plurality of third devicesreceive a same third signal.
 11. The central controller of claim 1,wherein the third data in at least one of the third signals includes theoccupancy estimate for at least one region of the building.
 12. Thecentral controller of claim 1, further comprising a mobile devicelocation estimator operable to generate an estimate of location of theone or more mobile devices, and wherein the mobile device locationestimate is based at least in part on information in first signals fromthe one or more fixed wireless sensors associated with a building. 13.The central controller of claim 1, wherein the central controller islocated within one of the one or more mobile wireless devices.
 14. Thecentral controller of claim 1, wherein the occupancy estimator furthercomprises: a proximity estimator operable to generate estimates of theproximity of a person to the one or more mobile wireless devices, basedin part on proximity indications in second signals; and a mobile devicelocation estimator operable to generate one or more mobile devicelocation estimates of the one or more mobile devices based at least inpart on the mobile device location indication in second signals, whereinthe one or more weights are based in part on the proximity estimates,and wherein the occupancy estimate for one or more regions of thebuilding are based on the weights, the one or more mobile devicelocation estimates and the aggregated first data.
 15. The centralcontroller of claim 1, further comprising: a third device selectoroperable to select the one or more third devices based on the occupancyestimate for one or more regions of the building, and wherein theproximity indication in second signals is proximity data generated bythe mobile wireless device; and wherein the proximity data in the secondsignals is based on readings from one or more analog sensors from a setof technologies consisting of: motion sensing, acceleration sensing,vibration sensing, direction sensing, sound sensing and light levelsensing.
 16. The central controller of claim 1, wherein the one or morethird devices are building automation devices located in the buildingand wherein the one or more third signals are short range wirelesssignals.
 17. The central controller of claim 1, wherein the one or morethird devices are media distribution devices located in the building.18. A method for transmitting signals, based on building occupancyinformation, the method comprising: receiving at a central controllerfirst signals from one or more fixed wireless sensors associated with abuilding, each first signal comprising first data, and first data beingindicative of occupancy of one or more regions of the building;receiving at a central controller second signals from one or more mobilewireless devices, wherein second signals contain a proximity indication,indicative of the proximity of one or more people to one of the one ormore mobile wireless devices, and second signals contain a mobile devicelocation indication, indicative of the location of one of the one ormore mobile wireless devices; estimating by the central controller oneor more mobile device location estimates, based at least in part on amobile device location indication in second signals; determining by thecentral controller one or more weights for one or more mobile devicelocation estimates, wherein the weights are determined based at least inpart on the proximity indication in second signals; generating by thecentral controller an occupancy estimate for one or more regions of thebuilding, based at least in part on first data and the generatedweights; and transmitting from the central controller a third signal toone or more third devices, wherein third signals contain third data andwherein third data is based in part on an occupancy estimate for one ormore regions of the building.
 19. The method of claim 18, furthercomprising: aggregating, at a location operably coupled to the centralcontroller, first signals across one or more fixed wireless sensors andsecond signals across the one or more mobile wireless devices andwherein at least some of the weights are calculated based on theaggregated second signals.
 20. The method of claim 18 furthercomprising: accessing a proximity criterion based on the proximityindication in one or more second signals; and determining whether theproximity criterion is satisfied by the proximity indication in one ormore second signals and calculating the weights for at least some of themobile device location estimates, based on the result of thedetermination, wherein the third data is based at least in part onwhether the proximity criterion is satisfied.
 21. The method of claim 18further comprising, upon determining that the proximity criterion issatisfied, identifying a setting for at least one third device, whereinthe third data identifies the setting.
 22. A method for send signals todevices, based on building occupancy information, the method comprising:identifying, by one or more fixed wireless sensors, a centralcontroller; performing by or more fixed wireless sensors, a pairingactivity with the central controller; recording at one or more fixedwireless sensors, first data indicative of occupancy of a region of thebuilding; receiving at a central controller first signals from one ormore fixed wireless sensors associated with a building, each firstsignal comprising first data, and first data being indicative ofoccupancy of one or more regions of the building; receiving at a centralcontroller second signals from a mobile wireless device, wherein secondsignals contain a proximity indication, indicative of the proximity ofone or more people to the mobile wireless device, and wherein secondsignals contain a mobile device location indication, indicative of thelocation of the mobile wireless device; determining by the centralcontroller one or more weights for the mobile device location indicationin one or more second signals, wherein the weights are determined basedat least in part on the proximity indication in second signals;generating at a central controller an occupancy estimate for one or moreregions of the building, based at least in part on first data and thegenerated weights; selecting by a central controller one or more thirddevices, based at least in part on the occupancy estimate for one ormore regions of the building; and transmitting from the centralcontroller a third signal to one or more third devices.
 23. The methodof claim 22, further comprising: assessing at the central controller anactivity criterion; determining at the central controller if theactivity criterion is satisfied, based on first data in first signals;and transmitting by the central controller, upon satisfaction of theactivity criterion, an advertising signal, wherein the advertisingsignal contains data operable to request mobile wireless devices totransmit second signals.
 24. The method of claim 22, further comprising:assessing at the central controller a proximity criterion; determiningat the central controller if the proximity criterion is satisfied, basedon the proximity indication in one or more second signals; and upondetermination that the proximity criterion is satisfied, storing in alocation model at least some of the first data or one or more aspects ofmobile device location indication from one or more second signals. 25.The method of claim 22, further comprising: generating at the centralcontroller one or more identity estimates associated with an occupancyestimate. assessing at the central controller an identity criterion;determining at the central controller if the identity criterion issatisfied by one or more of the identity estimates; and upondetermination that the identity criterion is satisfied by an identityestimate, modifying at least some of the data in a historical userprofile.
 26. The method of claim 23, further comprising: selecting bythe central controller one or more third device based at least in parton data from a historical user profile.